diff --git a/bootstrap/lecture/Makefile b/bootstrap/lecture/Makefile
index f7f02ba..de4182d 100644
--- a/bootstrap/lecture/Makefile
+++ b/bootstrap/lecture/Makefile
@@ -1,22 +1,29 @@
 BASENAME=bootstrap
+
 PYFILES=$(wildcard *.py)
 PYPDFFILES=$(PYFILES:.py=.pdf)
 
-pdf : $(BASENAME)-chapter.pdf $(PYPDFFILES)
+all : pdf 
+
+# script:
+pdf : $(BASENAME)-chapter.pdf
 
-$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex
+$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex $(PYPDFFILES)
 	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
 
 $(PYPDFFILES) : %.pdf : %.py
 	python $<
 
 clean :
-	rm -f *~ $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out $(BASENAME).aux $(BASENAME).log
+	rm -f *~ 
+	rm -f $(BASENAME).aux $(BASENAME).log
+	rm -f $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out
+	rm -f $(PYPDFFILES) $(GPTTEXFILES)
 
 cleanall : clean
 	rm -f $(BASENAME)-chapter.pdf
 
-watch :
+watchpdf :
 	while true; do ! make -q pdf && make pdf; sleep 0.5; done
 
 
diff --git a/likelihood/lecture/Makefile b/likelihood/lecture/Makefile
index 4e6367b..42b8e3d 100644
--- a/likelihood/lecture/Makefile
+++ b/likelihood/lecture/Makefile
@@ -1,22 +1,29 @@
 BASENAME=likelihood
+
 PYFILES=$(wildcard *.py)
 PYPDFFILES=$(PYFILES:.py=.pdf)
 
-pdf : $(BASENAME)-chapter.pdf $(PYPDFFILES)
+all : pdf 
+
+# script:
+pdf : $(BASENAME)-chapter.pdf
 
-$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex
+$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex $(PYPDFFILES)
 	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
 
 $(PYPDFFILES) : %.pdf : %.py
 	python $<
 
 clean :
-	rm -f *~ $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out $(BASENAME).aux $(BASENAME).log
+	rm -f *~ 
+	rm -f $(BASENAME).aux $(BASENAME).log
+	rm -f $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out
+	rm -f $(PYPDFFILES) $(GPTTEXFILES)
 
 cleanall : clean
 	rm -f $(BASENAME)-chapter.pdf
 
-watch :
+watchpdf :
 	while true; do ! make -q pdf && make pdf; sleep 0.5; done
 
 
diff --git a/likelihood/lecture/likelihood.tex b/likelihood/lecture/likelihood.tex
index 752d659..b69c5ec 100644
--- a/likelihood/lecture/likelihood.tex
+++ b/likelihood/lecture/likelihood.tex
@@ -1,11 +1,11 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\chapter{\tr{Maximum likelihood estimation}{Maximum-Likelihood Methode}}
+\chapter{\tr{Maximum likelihood estimation}{Maximum-Likelihood-Sch\"atzer}}
 
 In vielen Situationen wollen wir einen oder mehrere Parameter $\theta$
 einer Wahrscheinlichkeitsverteilung sch\"atzen, so dass die Verteilung
-die Daten $x_1, x_2, \ldots x_n$ am besten beschreibt. Bei der
-Maximum-Likelihood-Methode w\"ahlen wir die Parameter so, dass die
+die Daten $x_1, x_2, \ldots x_n$ am besten beschreibt. 
+Maximum-Likelihood-Sch\"atzer w\"ahlen die Parameter so, dass die
 Wahrscheinlichkeit, dass die Daten aus der Verteilung stammen, am
 gr\"o{\ss}ten ist.
 
@@ -16,10 +16,9 @@ $\theta$'') die Wahrscheinlichkeits(dichte)verteilung von $x$ mit dem
 Parameter(n) $\theta$. Das k\"onnte die Normalverteilung 
 \begin{equation}
   \label{normpdfmean}
-  p(x|\theta) = \frac{1}{\sqrt{2\pi \sigma^2}}e^{-\frac{(x-\theta)^2}{2\sigma^2}}
+  p(x|\theta) = \frac{1}{\sqrt{2\pi \sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}
 \end{equation}
-sein mit
-fester Standardverteilung $\sigma$ und dem Mittelwert $\mu$ als
+sein mit dem Mittelwert $\mu$ und der Standardabweichung $\sigma$ als
 Parameter $\theta$.
 
 Wenn nun den $n$ unabh\"angigen Beobachtungen $x_1, x_2, \ldots x_n$
@@ -59,9 +58,10 @@ das Maximum der logarithmierten Likelihood (``Log-Likelihood'') gesucht:
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \subsection{Beispiel: Das arithmetische Mittel}
 
-Wenn die Me{\ss}daten $x_1, x_2, \ldots x_n$ der Normalverteilung \eqnref{normpdfmean}
-entstammen, und wir den Mittelwert $\mu$ als einzigen Parameter der Verteilung betrachten,
-welcher Wert von $\theta$ maximiert dessen Likelhood?
+Wenn die Me{\ss}daten $x_1, x_2, \ldots x_n$ der Normalverteilung
+\eqnref{normpdfmean} entstammen, und wir den Mittelwert $\mu=\theta$ als
+einzigen Parameter der Verteilung betrachten, welcher Wert von
+$\theta$ maximiert dessen Likelhood?
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{mlemean}
@@ -89,7 +89,7 @@ nach dem Parameter $\theta$ und setzen diese gleich Null:
   \Leftrightarrow \quad n \theta & = & \sum_{i=1}^n x_i \\
   \Leftrightarrow \quad \theta & = & \frac{1}{n} \sum_{i=1}^n x_i
 \end{eqnarray*}
-Der Maximum-Likelihood-Estimator ist das arithmetische Mittel der Daten. D.h.
+Der Maximum-Likelihood-Sch\"atzer ist das arithmetische Mittel der Daten. D.h.
 das arithmetische Mittel maximiert die Wahrscheinlichkeit, dass die Daten aus einer
 Normalverteilung mit diesem Mittelwert gezogen worden sind.
 
@@ -101,12 +101,12 @@ Normalverteilung mit diesem Mittelwert gezogen worden sind.
   die Log-Likelihood (aus der Summe der logarithmierten
   Wahrscheinlichkeiten) f\"ur den Mittelwert als Parameter. Vergleiche
   die Position der Maxima mit den aus den Daten berechneten
-  Mittelwerte.
+  Mittelwert.
 \end{exercise}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Kurvenfit als Maximum Likelihood Estimation}
+\section{Kurvenfit als Maximum-Likelihood Sch\"atzung}
 Beim Kurvenfit soll eine Funktion $f(x;\theta)$ mit den Parametern
 $\theta$ an die Datenpaare $(x_i|y_i)$ durch Anpassung der Parameter
 $\theta$ gefittet werden. Wenn wir annehmen, dass die $y_i$ um die
@@ -125,25 +125,29 @@ gegeben sind.
 Der Parameter $\theta$ soll so gew\"ahlt werden, dass die
 Log-Likelihood maximal wird.  Der erste Term der Summe ist
 unabh\"angig von $\theta$ und kann deshalb bei der Suche nach dem
-Maximum weggelassen werden.
+Maximum weggelassen werden:
 \begin{eqnarray*}
   & = & - \frac{1}{2} \sum_{i=1}^n \left( \frac{y_i-f(x_i;\theta)}{\sigma_i} \right)^2
 \end{eqnarray*}
 Anstatt nach dem Maximum zu suchen, k\"onnen wir auch das Vorzeichen der Log-Likelihood
-umdrehen und nach dem Minimum suchen. Dabei k\"onnen wir auch den Faktor $1/2$ vor der Summe vernachl\"assigen --- auch das \"andert nichts an der Position des Minimums.
+umdrehen und nach dem Minimum suchen. Dabei k\"onnen wir auch den Faktor $1/2$ vor der Summe vernachl\"assigen --- auch das \"andert nichts an der Position des Minimums:
 \begin{equation}
+  \label{chisqmin}
   \theta_{mle} = \text{argmin}_{\theta} \; \sum_{i=1}^n \left( \frac{y_i-f(x_i;\theta)}{\sigma_i} \right)^2 \;\; = \;\; \text{argmin}_{\theta} \; \chi^2
 \end{equation}
-Die Summer der quadratischen Abst\"ande normiert auf die jeweiligen
+Die Summe der quadratischen Abst\"ande normiert auf die jeweiligen
 Standardabweichungen wird auch mit $\chi^2$ bezeichnet. Der Wert des
-Parameters $\theta$ welcher den quadratischen Abstand minimiert ist
+Parameters $\theta$, welcher den quadratischen Abstand minimiert, ist
 also identisch mit der Maximierung der Wahrscheinlichkeit, dass die
 Daten tats\"achlich aus der Funktion stammen k\"onnen. Minimierung des
-$\chi^2$ ist also ein Maximum-Likelihood Estimate.
+$\chi^2$ ist also eine Maximum-Likelihood Sch\"atzung. Aber nur, wenn
+die Daten normalverteilt um die Funktion streuen! Bei anderen
+Verteilungen m\"usste man die Log-Likelihood entsprechend
+\eqnref{loglikelihood} ausrechnen und maximieren.
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{mlepropline}
-  \caption{\label{mleproplinefig} Maximum Likelihood Estimation der
+  \caption{\label{mleproplinefig} Maximum-Likelihood Sch\"atzung der
     Steigung einer Ursprungsgeraden.}
 \end{figure}
 
@@ -165,33 +169,34 @@ und setzen diese gleich Null:
 \end{eqnarray}
 Damit haben wir nun einen anlytischen Ausdruck f\"ur die Bestimmung
 der Steigung $\theta$ des Regressionsgeraden gewonnen. Ein
-Gradientenabstieg ist f\"ur das Fitten der Geradensteigung also gar nicht
-n\"otig. Das gilt allgemein f\"ur das Fitten von Koeffizienten von
-linear kombinierten Basisfunktionen. Parameter die nichtlinear in
-einer Funktion enthalten sind k\"onnen aber nicht analytisch aus den
-Daten berechnet werden. Da bleibt dann nur auf numerische Verfahren
-zur Optimierung der Kostenfunktion, wie z.B. der Gradientenabstieg,
-zur\"uckzugreifen.
+Gradientenabstieg ist f\"ur das Fitten der Geradensteigung also gar
+nicht n\"otig. Das gilt allgemein f\"ur das Fitten von Koeffizienten
+von linear kombinierten Basisfunktionen. Parameter, die nichtlinear in
+einer Funktion enthalten sind, k\"onnen im Gegensatz dazu nicht
+analytisch aus den Daten berechnet werden. F\"ur diesen Fall bleibt
+dann nur auf numerische Verfahren zur Optimierung der Kostenfunktion,
+wie z.B. der Gradientenabstieg, zur\"uckzugreifen.
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Fits von Wahrscheinlichkeitsverteilungen}
 Zum Abschluss betrachten wir noch den Fall, bei dem wir die Parameter
 einer Wahrscheinlichkeitsdichtefunktion (z.B. Mittelwert und
-Standardabweichung der Normalverteilung) an ein Datenset fitten wolle.
+Standardabweichung der Normalverteilung) an ein Datenset fitten wollen.
 
 Ein erster Gedanke k\"onnte sein, die
 Wahrscheinlichkeitsdichtefunktion durch Minimierung des quadratischen
-Abstands an ein Histogram der Daten zu fitten. Das ist aber aus
+Abstands an ein Histogramm der Daten zu fitten. Das ist aber aus
 folgenden Gr\"unden nicht die Methode der Wahl: (i)
 Wahrscheinlichkeitsdichten k\"onnen nur positiv sein. Darum k\"onnen
 insbesondere bei kleinen Werten die Daten nicht symmetrisch streuen,
-wie es normalverteilte Daten machen sollten. (ii) Die Datenwerte sind
-nicht unabh\"angig, da das normierte Histogram sich zu Eins
-aufintegriert. Die beiden Annahmen normalverteilte und unabh\"angige Daten
-die die Minimierung des quadratischen Abstands zu einem Maximum
-Likelihood Estimator machen sind also verletzt. (iii) Das Histgramm
-h\"angt von der Wahl der Klassenbreite ab.
+wie es bei normalverteilten Daten der Fall ist. (ii) Die Datenwerte
+sind nicht unabh\"angig, da das normierte Histogram sich zu Eins
+aufintegriert. Die beiden Annahmen normalverteilte und unabh\"angige
+Daten, die die Minimierung des quadratischen Abstands
+\eqnref{chisqmin} zu einem Maximum-Likelihood Sch\"atzer machen, sind
+also verletzt. (iii) Das Histogramm h\"angt von der Wahl der
+Klassenbreite ab.
 
 Den direkten Weg, eine Wahrscheinlichkeitsdichtefunktion an ein
 Datenset zu fitten, haben wir oben schon bei dem Beispiel zur
@@ -204,9 +209,10 @@ z.B. dem Gradientenabstieg, gel\"ost wird.
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{mlepdf}
-  \caption{\label{mlepdffig} Maximum Likelihood Estimation einer
-    Wahrscheinlichkeitsdichtefunktion. Links: die 100 Datenpunkte, die aus der Gammaverteilung
-    2. Ordnung (rot) gezogen worden sind. Der Maximum-Likelihood-Fit ist orange dargestellt.
-    Rechts: das normierte Histogramm der Daten zusammen mit der \"uber Minimierung
-    des quadratischen Abstands zum Histogramm berechneten Fits ist potentiell schlechter.}
+  \caption{\label{mlepdffig} Maximum-Likelihood Sch\"atzung einer
+    Wahrscheinlichkeitsdichtefunktion. Links: die 100 Datenpunkte, die
+    aus der Gammaverteilung 2. Ordnung (rot) gezogen worden sind. Der
+    Maximum-Likelihood-Fit ist orange dargestellt.  Rechts: das
+    normierte Histogramm der Daten zusammen mit dem \"uber Minimierung
+    des quadratischen Abstands zum Histogramm berechneten Fit.}
 \end{figure}
diff --git a/pointprocesses/exercises/Makefile b/pointprocesses/exercises/Makefile
new file mode 100644
index 0000000..0b63fb9
--- /dev/null
+++ b/pointprocesses/exercises/Makefile
@@ -0,0 +1,35 @@
+BASENAME=pointprocesses
+TEXFILES=$(wildcard $(BASENAME)??.tex)
+EXERCISES=$(TEXFILES:.tex=.pdf)
+SOLUTIONS=$(EXERCISES:pointprocesses%=pointprocesses-solutions%)
+
+.PHONY: pdf exercises solutions watch watchexercises watchsolutions clean
+
+pdf : $(SOLUTIONS) $(EXERCISES)
+
+exercises : $(EXERCISES)
+
+solutions : $(SOLUTIONS)
+
+$(SOLUTIONS) : pointprocesses-solutions%.pdf : pointprocesses%.tex instructions.tex
+	{ echo "\\documentclass[answers,12pt,a4paper,pdftex]{exam}"; sed -e '1d' $<; } > $(patsubst %.pdf,%.tex,$@)
+	pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $(patsubst %.pdf,%.tex,$@) || true
+	rm $(patsubst %.pdf,%,$@).[!p]*
+
+$(EXERCISES) : %.pdf : %.tex instructions.tex
+	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
+
+watch :
+	while true; do ! make -q pdf && make pdf; sleep 0.5; done
+
+watchexercises :
+	while true; do ! make -q exercises && make exercises; sleep 0.5; done
+
+watchsolutions :
+	while true; do ! make -q solutions && make solutions; sleep 0.5; done
+
+clean :
+	rm -f *~ *.aux *.log *.out
+
+cleanup : clean
+	rm -f $(SOLUTIONS) $(EXERCISES)
diff --git a/pointprocesses/exercises/instructions.tex b/pointprocesses/exercises/instructions.tex
new file mode 100644
index 0000000..96ac4bc
--- /dev/null
+++ b/pointprocesses/exercises/instructions.tex
@@ -0,0 +1,11 @@
+\vspace*{-6.5ex}
+\begin{center}
+\textbf{\Large Einf\"uhrung in die wissenschaftliche Datenverarbeitung}\\[1ex]
+{\large Jan Grewe, Jan Benda}\\[-3ex]
+Abteilung Neuroethologie \hfill --- \hfill Institut f\"ur Neurobiologie \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
+\end{center}
+
+\ifprintanswers%
+\else
+
+\fi
diff --git a/pointprocesses/exercises/pointprocesses01.tex b/pointprocesses/exercises/pointprocesses01.tex
new file mode 100644
index 0000000..b4d927c
--- /dev/null
+++ b/pointprocesses/exercises/pointprocesses01.tex
@@ -0,0 +1,202 @@
+\documentclass[12pt,a4paper,pdftex]{exam}
+
+\usepackage[german]{babel}
+\usepackage{pslatex}
+\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+\usepackage{xcolor}
+\usepackage{graphicx}
+\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+
+%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
+\pagestyle{headandfoot}
+\ifprintanswers
+\newcommand{\stitle}{: L\"osungen}
+\else
+\newcommand{\stitle}{}
+\fi
+\header{{\bfseries\large \"Ubung 6\stitle}}{{\bfseries\large Statistik}}{{\bfseries\large 27. Oktober, 2015}}
+\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
+jan.benda@uni-tuebingen.de}
+\runningfooter{}{\thepage}{}
+
+\setlength{\baselineskip}{15pt}
+\setlength{\parindent}{0.0cm}
+\setlength{\parskip}{0.3cm}
+\renewcommand{\baselinestretch}{1.15}
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+  language=Matlab,
+  basicstyle=\ttfamily\footnotesize,
+  numbers=left,
+  numberstyle=\tiny,
+  title=\lstname,
+  showstringspaces=false,
+  commentstyle=\itshape\color{darkgray},
+  breaklines=true,
+  breakautoindent=true,
+  columns=flexible,
+  frame=single,
+  xleftmargin=1em,
+  xrightmargin=1em,
+  aboveskip=10pt
+}
+
+%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{bm} 
+\usepackage{dsfont}
+\newcommand{\naZ}{\mathds{N}}
+\newcommand{\gaZ}{\mathds{Z}}
+\newcommand{\raZ}{\mathds{Q}}
+\newcommand{\reZ}{\mathds{R}}
+\newcommand{\reZp}{\mathds{R^+}}
+\newcommand{\reZpN}{\mathds{R^+_0}}
+\newcommand{\koZ}{\mathds{C}}
+
+%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\newcommand{\continue}{\ifprintanswers%
+\else
+\vfill\hspace*{\fill}$\rightarrow$\newpage%
+\fi}
+\newcommand{\continuepage}{\ifprintanswers%
+\newpage
+\else
+\vfill\hspace*{\fill}$\rightarrow$\newpage%
+\fi}
+\newcommand{\newsolutionpage}{\ifprintanswers%
+\newpage%
+\else
+\fi}
+
+%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\newcommand{\qt}[1]{\textbf{#1}\\}
+\newcommand{\pref}[1]{(\ref{#1})}
+\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
+\newcommand{\code}[1]{\texttt{#1}}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document}
+
+\input{instructions}
+
+
+\begin{questions}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\question \qt{Homogeneous Poisson process}
+We use the Poisson process to generate spike trains on which we can test and imrpove some 
+standard analysis functions.
+
+A homogeneous Poisson process of rate $\lambda$ (measured in Hertz) is a point process
+where the probability of an event is independent of time $t$ and independent of previous events.
+The probability $P$ of an event within a bin of width $\Delta t$ is 
+\[ P = \lambda \cdot \Delta t \]
+for sufficiently small $\Delta t$.
+\begin{parts}
+  
+  \part Write a function that generates $n$ homogeneous Poisson spike trains of a given duration $T_{max}$
+  with rate $\lambda$.
+  \begin{solution}
+    \lstinputlisting{hompoissonspikes.m}
+  \end{solution}
+  
+  \part Using this function, generate a few trials and display them in a raster plot.
+  \begin{solution}
+    \lstinputlisting{../code/spikeraster.m}
+    \begin{lstlisting}
+      spikes = hompoissonspikes( 10, 100.0, 0.5 );
+      spikeraster( spikes )
+    \end{lstlisting}
+    \mbox{}\\[-3ex]
+    \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
+  \end{solution}
+  
+  \part Write a function that extracts a single vector of interspike intervals
+  from the spike times returned by the first function.
+  \begin{solution}
+    \lstinputlisting{../code/isis.m}
+  \end{solution}
+  
+  \part Write a function that plots the interspike-interval histogram
+  from a vector of interspike intervals. The function should also
+  compute the mean, the standard deviation, and the CV of the intervals
+  and display the values in the plot.
+  \begin{solution}
+    \lstinputlisting{../code/isihist.m}
+  \end{solution}
+  
+  \part Compute histograms for Poisson spike trains with rate
+  $\lambda=100$\,Hz. Play around with $T_{max}$ and $n$ and the bin width
+  (start with 1\,ms) of the histogram.
+  How many
+  interspike intervals do you approximately need to get a ``nice''
+  histogram? How long do you need to record from the neuron?
+  \begin{solution}
+    About 5000 intervals for 25 bins. This corresponds to a $5000 / 100\,\hertz = 50\,\second$ recording
+    of a neuron firing with 100\,\hertz.
+  \end{solution}
+  
+  \part Compare the histogram with the true distribution of intervals $T$ of the Poisson process
+  \[ p(T) = \lambda e^{-\lambda T} \]
+  for various rates $\lambda$.
+  \begin{solution}
+    \lstinputlisting{hompoissonisih.m}
+    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
+    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
+  \end{solution}
+  
+  \part What happens if you make the bin width of the histogram smaller than $\Delta t$ 
+  used for generating the Poisson spikes?
+  \begin{solution}
+    The bins between the discretization have zero entries. Therefore
+    the other ones become higher than they should be.
+  \end{solution}
+  
+  \part Plot the mean interspike interval, the corresponding standard deviation, and the CV
+  as a function of the rate $\lambda$ of the Poisson process.
+  Compare the ../code with the theoretical expectations for the dependence on $\lambda$.
+  \begin{solution}
+    \lstinputlisting{hompoissonisistats.m}
+    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
+  \end{solution}
+  
+  \part Write a function that computes serial correlations for the interspike intervals
+  for a range of lags.
+  The serial correlations $\rho_k$ at lag $k$ are defined as
+  \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
+  Use this function to show that interspike intervals of Poisson spikes are independent.
+  \begin{solution}
+    \lstinputlisting{../code/isiserialcorr.m}
+    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonserial100hz}}
+  \end{solution}
+  
+  \part Write a function that generates from spike times 
+  a histogram of spike counts in a count window of given duration $W$.
+  The function should also plot the Poisson distribution
+  \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
+  for the rate $\lambda$ determined from the spike trains.
+  \begin{solution}
+    \lstinputlisting{../code/counthist.m}
+    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}}
+    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}}
+  \end{solution}
+  
+  \part Write a function that computes mean count, variance of count and the corresponding Fano factor
+  for a range of count window durations. The function should generate tow plots: one plotting
+  the count variance against the mean, the other one the Fano factor as a function of the window duration.
+  \begin{solution}
+    \lstinputlisting{../code/fano.m}
+    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonfano100hz}}
+  \end{solution}
+  
+\end{parts}
+
+\end{questions}
+
+\end{document}
\ No newline at end of file
diff --git a/pointprocesses/exercises/poisson.pdf b/pointprocesses/exercises/poisson.pdf
deleted file mode 100644
index aa1756e..0000000
Binary files a/pointprocesses/exercises/poisson.pdf and /dev/null differ
diff --git a/pointprocesses/exercises/poisson.tex b/pointprocesses/exercises/poisson.tex
deleted file mode 100644
index 4b8f0f0..0000000
--- a/pointprocesses/exercises/poisson.tex
+++ /dev/null
@@ -1,160 +0,0 @@
-\documentclass[addpoints,10pt]{exam}
-\usepackage{url}
-\usepackage{color}
-\usepackage{hyperref}
-\usepackage{graphicx}
-
-\pagestyle{headandfoot}
-\runningheadrule
-\firstpageheadrule
-
-\firstpageheader{Scientific Computing}{Homogeneous Poisson process}{Oct 27, 2014}
-%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
-\firstpagefooter{}{}{}
-\runningfooter{}{}{}
-\pointsinmargin
-\bracketedpoints
-
-%\printanswers
-\shadedsolutions
-
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
- basicstyle=\ttfamily,
- numbers=left,
- showstringspaces=false,
- language=Matlab,
- breaklines=true,
- breakautoindent=true,
- columns=flexible,
- frame=single,
- captionpos=t,
- xleftmargin=2em,
- xrightmargin=1em,
- aboveskip=10pt,
- %title=\lstname,
- title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
- }
-
-
-\begin{document}
-
-\sffamily
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{questions}
-  \question \textbf{Homogeneous Poisson process}
-  We use the Poisson process to generate spike trains on which we can test and imrpove some 
-  standard analysis functions.
-
-  A homogeneous Poisson process of rate $\lambda$ (measured in Hertz) is a point process
-  where the probability of an event is independent of time $t$ and independent of previous events.
-  The probability $P$ of an event within a bin of width $\Delta t$ is 
-  \[ P = \lambda \cdot \Delta t \]
-  for sufficiently small $\Delta t$.
-  \begin{parts}
-
-    \part Write a function that generates $n$ homogeneous Poisson spike trains of a given duration $T_{max}$
-    with rate $\lambda$.
-    \begin{solution}
-      \lstinputlisting{hompoissonspikes.m}
-    \end{solution}
-
-    \part Using this function, generate a few trials and display them in a raster plot.
-    \begin{solution}
-      \lstinputlisting{simulations/spikeraster.m}
-      \begin{lstlisting}
-spikes = hompoissonspikes( 10, 100.0, 0.5 );
-spikeraster( spikes )
-      \end{lstlisting}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
-    \end{solution}
-
-    \part Write a function that extracts a single vector of interspike intervals
-    from the spike times returned by the first function.
-    \begin{solution}
-      \lstinputlisting{simulations/isis.m}
-    \end{solution}
-
-    \part Write a function that plots the interspike-interval histogram
-    from a vector of interspike intervals. The function should also
-    compute the mean, the standard deviation, and the CV of the intervals
-    and display the values in the plot.
-    \begin{solution}
-      \lstinputlisting{simulations/isihist.m}
-    \end{solution}
-
-    \part Compute histograms for Poisson spike trains with rate
-    $\lambda=100$\,Hz. Play around with $T_{max}$ and $n$ and the bin width
-    (start with 1\,ms) of the histogram.
-    How many
-    interspike intervals do you approximately need to get a ``nice''
-    histogram? How long do you need to record from the neuron?
-    \begin{solution}
-      About 5000 intervals for 25 bins. This corresponds to a $5000 / 100\,\hertz = 50\,\second$ recording
-      of a neuron firing with 100\,\hertz.
-    \end{solution}
-
-    \part Compare the histogram with the true distribution of intervals $T$ of the Poisson process
-    \[ p(T) = \lambda e^{-\lambda T} \]
-    for various rates $\lambda$.
-    \begin{solution}
-      \lstinputlisting{hompoissonisih.m}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
-    \end{solution}
-
-    \part What happens if you make the bin width of the histogram smaller than $\Delta t$ 
-    used for generating the Poisson spikes?
-    \begin{solution}
-      The bins between the discretization have zero entries. Therefore
-      the other ones become higher than they should be.
-    \end{solution}
-
-    \part Plot the mean interspike interval, the corresponding standard deviation, and the CV
-    as a function of the rate $\lambda$ of the Poisson process.
-    Compare the simulations with the theoretical expectations for the dependence on $\lambda$.
-    \begin{solution}
-      \lstinputlisting{hompoissonisistats.m}
-      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
-    \end{solution}
-
-    \part Write a function that computes serial correlations for the interspike intervals
-    for a range of lags.
-    The serial correlations $\rho_k$ at lag $k$ are defined as
-    \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
-    Use this function to show that interspike intervals of Poisson spikes are independent.
-    \begin{solution}
-      \lstinputlisting{simulations/isiserialcorr.m}
-      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonserial100hz}}
-    \end{solution}
-
-    \part Write a function that generates from spike times 
-    a histogram of spike counts in a count window of given duration $W$.
-    The function should also plot the Poisson distribution
-    \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
-    for the rate $\lambda$ determined from the spike trains.
-    \begin{solution}
-      \lstinputlisting{simulations/counthist.m}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}}
-    \end{solution}
-
-    \part Write a function that computes mean count, variance of count and the corresponding Fano factor
-    for a range of count window durations. The function should generate tow plots: one plotting
-    the count variance against the mean, the other one the Fano factor as a function of the window duration.
-    \begin{solution}
-      \lstinputlisting{simulations/fano.m}
-      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonfano100hz}}
-    \end{solution}
-
-  \end{parts}
-  
-\end{questions}
-
-
-\end{document}
diff --git a/pointprocesses/lecture/Makefile b/pointprocesses/lecture/Makefile
index 7a243a5..4ac656e 100644
--- a/pointprocesses/lecture/Makefile
+++ b/pointprocesses/lecture/Makefile
@@ -1,142 +1,70 @@
 BASENAME=pointprocesses
 
-TEXFILE=$(BASENAME).tex
-DVIFILE=$(BASENAME).dvi
-PSFILE=$(BASENAME).ps
-PDFFILE=$(BASENAME).pdf
-
-FOILSFILE=foils.pdf
-THUMBNAILSFILE=thumbnails.pdf
-
-HTMLBASENAME=$(BASENAME)h
-HTMLTEXFILE=$(BASENAME)h.tex
-HTMLDIR=$(BASENAME)h
+PYFILES=$(wildcard *.py)
+PYPDFFILES=$(PYFILES:.py=.pdf)
 
 GPTFILES=$(wildcard *.gpt)
 GPTTEXFILES=$(GPTFILES:.gpt=.tex)
 
 
-all: ps pdf talk again watchps watchpdf foils thumbs html html1 epsfigs clean cleanup cleanplots help
-.PHONY: epsfigs
+all: pdf slides thumbs
+
+# script:
+pdf : $(BASENAME)-chapter.pdf
+$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex $(GPTTEXFILES) $(PYPDFFILES)
+	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
 
 
+# slides:
+slides: $(BASENAME)-slides.pdf
+$(BASENAME)-slides.pdf : $(BASENAME)-slides.tex $(GPTTEXFILES) $(PYPDFFILES)
+	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
+
 # thumbnails:
-thumbs: $(THUMBNAILSFILE)
-$(THUMBNAILSFILE): $(TEXFILE) $(GPTTEXFILES)
+thumbs: $(BASENAME)-handout.pdf
+$(BASENAME)-handout.pdf: $(BASENAME)-slides.tex $(GPTTEXFILES)
 	sed -e 's/setboolean{presentation}{true}/setboolean{presentation}{false}/; s/usepackage{crop}/usepackage[frame]{crop}/' $< > thumbsfoils.tex
 	pdflatex thumbsfoils | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex thumbsfoils || true
-	pdfnup --nup 2x4 --no-landscape --paper a4paper --trim "-1cm -1cm -1cm -1cm" --outfile $@ thumbsfoils.pdf '1-19'
+	pdfnup --nup 2x4 --no-landscape --paper a4paper --trim "-1cm -1cm -1cm -1cm" --outfile $@ thumbsfoils.pdf # 1-19
 	rm thumbsfoils.*
 
-# transparencies:
-foils: $(FOILSFILE)
-$(FOILSFILE): $(TEXFILE) $(GPTTEXFILES)
-	sed -e 's/setboolean{presentation}{true}/setboolean{presentation}{false}/' $< > tfoils.tex
-	pdflatex tfoils | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex tfoils || true
-	pdfnup --nup 1x2 --orient portrait --trim "-1mm -1mm -1mm -1mm" --frame true --delta "1cm 1cm" --paper a4paper --outfile tfoils2.pdf tfoils.pdf
-	pdfnup --nup 1x1 --orient portrait --trim "-2cm -2cm -2cm -2cm" --paper a4paper --outfile $@ tfoils2.pdf 
-	rm tfoils.* tfoils2.pdf
-
-# talk:
-talk: $(PDFFILE)
-pdf: $(PDFFILE)
-$(PDFFILE): $(TEXFILE) $(GPTTEXFILES)
-	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
-# batchmode (no output, no stop on error)
-# nonstopmode / scrollmode (no stop on error)
-# errorstopmode (stop on error)
-
-
-again :
-	pdflatex $(TEXFILE)
-
 watchpdf :
 	while true; do ! make -q pdf && make pdf; sleep 0.5; done
 
-# html
-html : $(HTMLTEXFILE) $(GPTTEXFILES)
-	rm -f $(HTMLDIR)/*
-	htlatex $<
-	mkdir -p $(HTMLDIR)
-	mv $(HTMLBASENAME).html $(HTMLDIR)
-	mv $(HTMLBASENAME)*.* $(HTMLDIR)
-	mv z*.gif $(HTMLDIR)
-	cd $(HTMLDIR); for i in *.gif; do convert -page +0+0 $$i tmp.gif; mv tmp.gif $$i; done; rmtex $(HTMLBASENAME)
-
-#$(HTMLTEXFILE) : $(TEXFILE) Makefile
-#	sed 's/setboolean{html}{false}/setboolean{html}{true}/; s/\\colorbox{white}{\(.*\)}/\1/g' $< > $@
-
-html1 : $(HTMLTEXFILE) $(GPTTEXFILES)
-	latex2html -dir $(HTMLDIR) -mkdir -subdir -nonavigation -noinfo -image_type png -notransparent -white -split 0 $<
-	sed 's-<I>Date:</I>--' $(HTMLDIR)/$(HTMLDIR).html > tmp.html
-	cp tmp.html $(HTMLDIR)/index.html
-	mv tmp.html $(HTMLDIR)/$(HTMLDIR).html
-
-$(HTMLTEXFILE) : $(TEXFILE)
-	sed '/^%nohtml/,/^%endnohtml/d; s/\\colorbox{white}{\(.*\)}/\1/g' $< > $@
-
-
-# eps of all figures:
-epsfigs:
-	mkdir -p epsfigs; \
-	for i in $(GPTFILES); do \
-	  { sed -n -e '1,/\\begin{document}/p' $(TEXFILE); echo "\texpicture{$${i%%.*}}"; echo "\end{document}"; } > tmp.tex; \
-	  latex tmp.tex; \
-	  dvips tmp.dvi; \
-	  ps2eps tmp.ps; \
-	  mv tmp.eps epsfigs/$${i%%.*}.eps; \
-	  rm tmp.*; \
-	done
-
-
-# plots:
-%.tex: %.gpt whitestyles.gp
+watchslides :
+	while true; do ! make -q slides && make slides; sleep 0.5; done
+
+# python plots:
+$(PYPDFFILES) : %.pdf: %.py
+	python $<
+
+# gnuplot plots:
+$(GPTTEXFILES) : %.tex: %.gpt whitestyles.gp
 	gnuplot whitestyles.gp $<
 	epstopdf $*.eps
 
 
 clean :
-	rm -f *~
-	rmtex $(BASENAME)
-	rm -f $(GPTTEXFILES)
-
-cleanup :
-	rm -f *~
-	rmtex $(BASENAME)
-	rm -f $(PSFILE) $(PDFFILE) $(FOILSFILE) $(THUMBNAILSFILE)
-	rm -f $(GPTTEXFILES)
-	rm -f -r $(HTMLDIR)
-
-
-cleanplots :
-	sed -n -e '/\\begin{document}/,/\\end{document}/p' $(TEXFILE) | fgrep '\input{' | grep -v '^%' | sed 's/.*input{\(.*\).tex}.*/\1.gpt/' > plot.fls
-	mkdir -p unusedplots
-	for i in *.gp*; do \
-	  grep -q $$i plot.fls || { grep -q $$i $$(<plot.fls) && echo $$i || mv $$i unusedplots; }; \
-	done >> plot.fls
-	for i in $$(<plot.fls); do \
-	  sed "s/\([^'\" ]*\.dat\)/\n\1\n/g;" $$i | fgrep .dat; \
-	done | sort | uniq > dat.fls
-	mkdir -p unuseddata 
-	for i in *.dat; do \
-	  grep -q $$i dat.fls || mv $$i unuseddata; \
-	done
-	rm dat.fls plot.fls
+	rm -f *~ 
+	rm -f $(BASENAME).aux $(BASENAME).log
+	rm -f $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out
+	rm -f $(BASENAME)-slides.aux $(BASENAME)-slides.log $(BASENAME)-slides.out $(BASENAME)-slides.toc $(BASENAME)-slides.nav $(BASENAME)-slides.snm $(BASENAME)-slides.vrb
+	rm -f $(PYPDFFILES) $(GPTTEXFILES)
+
+cleanall : clean
+	rm -f $(BASENAME)-chapter.pdf $(BASENAME)-slides.pdf $(BASENAME)-handout.pdf
 
 
 help : 
 	@echo -e \
-	"make pdf:    make the pdf file of the talk.\n"\
-	"make foils:  make black&white postscript foils of the talk.\n"\
-	"make thumbs: make color thumbnails of the talk.\n"\
-	"make again: run latex and make the pdf file of the talk,\n"\
-        "            no matter whether you changed the .tex file or not.\n\n"\
-	"make watchpdf:   make the pdf file of the talk\n"\
+	"make pdf:        make the pdf file of the script.\n"\
+	"make slides:     make the pdf file of the slides.\n"\
+	"make thumbs:     make color thumbnails of the talk.\n"\
+	"make watchpdf:   make the pdf file of the script\n"\
+        "                 whenever the tex file is modified.\n"\
+	"make watchpdf:   make the pdf file of the slides\n"\
         "                 whenever the tex file is modified.\n"\
-	"make html:       make a html version of the paper (in $(HTMLDIR)).\n\n"\
 	"make clean:      remove all intermediate files,\n"\
-        "                 just leave the source files and the final .ps and .pdf files.\n"\
+        "                 just leave the source files and the final .pdf files.\n"\
 	"make cleanup:    remove all intermediate files as well as\n"\
-        "                 the final .ps and .pdf files.\n"\
-	"make cleanplots: move all unused .gpt and .dat files\n"\
-        "                 into unusedplots/ and unuseddata/, respectively."
+        "                 the final .pdf files.\n"\
diff --git a/pointprocesses/lecture/isihexamples.py b/pointprocesses/lecture/isihexamples.py
new file mode 100644
index 0000000..9d50fbc
--- /dev/null
+++ b/pointprocesses/lecture/isihexamples.py
@@ -0,0 +1,101 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def hompoisson(rate, trials, duration) :
+    spikes = []
+    for k in range(trials) :
+        times = []
+        t = 0.0
+        while t < duration :
+            t += np.random.exponential(1/rate)
+            times.append( t )
+        spikes.append( times )
+    return spikes
+
+def inhompoisson(rate, trials, dt) :
+    spikes = []
+    p = rate*dt
+    for k in range(trials) :
+        x = np.random.rand(len(rate))
+        times = dt*np.nonzero(x<p)[0]
+        spikes.append( times )
+    return spikes
+
+
+def pifspikes(input, trials, dt, D=0.1) :
+    vreset = 0.0
+    vthresh = 1.0
+    tau = 1.0
+    spikes = []
+    for k in range(trials) :
+        times = []
+        v = vreset
+        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
+        for k in xrange(len(noise)) :
+            v += (input[k]+noise[k])*dt/tau
+            if v >= vthresh :
+                v = vreset
+                times.append(k*dt)
+        spikes.append( times )
+    return spikes
+
+def isis( spikes ) :
+    isi = []
+    for k in xrange(len(spikes)) :
+        isi.extend(np.diff(spikes[k]))
+    return isi
+
+def plotisih( ax, isis, binwidth=None ) :
+    if binwidth == None :
+        nperbin = 200.0    # average number of isis per bin
+        bins = len(isis)/nperbin  # number of bins
+        binwidth = np.max(isis)/bins
+        if binwidth < 5e-4 :     # half a millisecond
+            binwidth = 5e-4
+    h, b = np.histogram(isis, np.arange(0.0, np.max(isis)+binwidth, binwidth), density=True)
+    ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.set_xlabel('ISI [ms]')
+    ax.set_ylabel('p(ISI) [1/s]')
+    ax.bar( 1000.0*b[:-1], h, 1000.0*np.diff(b) )
+
+# parameter:
+rate = 20.0
+drate = 50.0
+trials = 10
+duration = 100.0
+dt = 0.001
+tau = 0.1;
+
+# homogeneous spike trains:
+homspikes = hompoisson(rate, trials, duration)
+
+# OU noise:
+rng = np.random.RandomState(54637281)
+time = np.arange(0.0, duration, dt)
+x = np.zeros(time.shape)+rate
+n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+for k in xrange(1,len(x)) :
+    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+x[x<0.0] = 0.0
+
+# pif spike trains:
+inhspikes = pifspikes(x, trials, dt, D=0.3)
+
+fig = plt.figure( figsize=(9,4) )
+ax = fig.add_subplot(1, 2, 1)
+ax.set_title('stationary')
+ax.set_xlim(0.0, 200.0)
+ax.set_ylim(0.0, 40.0)
+plotisih(ax, isis(homspikes))
+
+ax = fig.add_subplot(1, 2, 2)
+ax.set_title('non-stationary')
+ax.set_xlim(0.0, 200.0)
+ax.set_ylim(0.0, 40.0)
+plotisih(ax, isis(inhspikes))
+
+plt.tight_layout()
+plt.savefig('isihexamples.pdf')
+plt.show()
diff --git a/pointprocesses/lecture/pointprocesses-chapter.tex b/pointprocesses/lecture/pointprocesses-chapter.tex
new file mode 100644
index 0000000..8ecbb10
--- /dev/null
+++ b/pointprocesses/lecture/pointprocesses-chapter.tex
@@ -0,0 +1,271 @@
+\documentclass[12pt]{report}
+
+%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\title{\tr{Introduction to Scientific Computing}{Einf\"uhrung in die wissenschaftliche Datenverarbeitung}}
+\author{Jan Benda\\Abteilung Neuroethologie\\[2ex]\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
+\date{WS 15/16}
+
+%%%% language %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% \newcommand{\tr}[2]{#1}  % en
+% \usepackage[english]{babel}
+\newcommand{\tr}[2]{#2}  % de
+\usepackage[german]{babel}
+
+%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{pslatex}   % nice font for pdf file
+\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+
+%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[left=25mm,right=25mm,top=20mm,bottom=30mm]{geometry}
+\setcounter{tocdepth}{1}
+
+%%%%% section style %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[sf,bf,it,big,clearempty]{titlesec}
+\setcounter{secnumdepth}{1}
+
+
+%%%%% units %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+ 
+
+%%%%% figures %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{graphicx}
+\usepackage{xcolor}
+\pagecolor{white}
+
+\newcommand{\ruler}{\par\noindent\setlength{\unitlength}{1mm}\begin{picture}(0,6)%
+  \put(0,4){\line(1,0){170}}%
+  \multiput(0,2)(10,0){18}{\line(0,1){4}}%
+  \multiput(0,3)(1,0){170}{\line(0,1){2}}%
+  \put(0,0){\makebox(0,0){{\tiny 0}}}%
+  \put(10,0){\makebox(0,0){{\tiny 1}}}%
+  \put(20,0){\makebox(0,0){{\tiny 2}}}%
+  \put(30,0){\makebox(0,0){{\tiny 3}}}%
+  \put(40,0){\makebox(0,0){{\tiny 4}}}%
+  \put(50,0){\makebox(0,0){{\tiny 5}}}%
+  \put(60,0){\makebox(0,0){{\tiny 6}}}%
+  \put(70,0){\makebox(0,0){{\tiny 7}}}%
+  \put(80,0){\makebox(0,0){{\tiny 8}}}%
+  \put(90,0){\makebox(0,0){{\tiny 9}}}%
+  \put(100,0){\makebox(0,0){{\tiny 10}}}%
+  \put(110,0){\makebox(0,0){{\tiny 11}}}%
+  \put(120,0){\makebox(0,0){{\tiny 12}}}%
+  \put(130,0){\makebox(0,0){{\tiny 13}}}%
+  \put(140,0){\makebox(0,0){{\tiny 14}}}%
+  \put(150,0){\makebox(0,0){{\tiny 15}}}%
+  \put(160,0){\makebox(0,0){{\tiny 16}}}%
+  \put(170,0){\makebox(0,0){{\tiny 17}}}%
+  \end{picture}\par}
+
+% figures:
+\setlength{\fboxsep}{0pt}
+\newcommand{\texpicture}[1]{{\sffamily\footnotesize\input{#1.tex}}}
+%\newcommand{\texpicture}[1]{\fbox{\sffamily\footnotesize\input{#1.tex}}}
+%\newcommand{\texpicture}[1]{\setlength{\fboxsep}{2mm}\fbox{#1}}
+%\newcommand{\texpicture}[1]{}
+\newcommand{\figlabel}[1]{\textsf{\textbf{\large \uppercase{#1}}}}
+
+% maximum number of floats:
+\setcounter{topnumber}{2}
+\setcounter{bottomnumber}{0}
+\setcounter{totalnumber}{2}
+
+% float placement fractions:
+\renewcommand{\textfraction}{0.2}
+\renewcommand{\topfraction}{0.8}
+\renewcommand{\bottomfraction}{0.0}
+\renewcommand{\floatpagefraction}{0.5}
+
+% spacing for floats:
+\setlength{\floatsep}{12pt plus 2pt minus 2pt}
+\setlength{\textfloatsep}{20pt plus 4pt minus 2pt}
+\setlength{\intextsep}{12pt plus 2pt minus 2pt}
+
+% spacing for a floating page:
+\makeatletter
+  \setlength{\@fptop}{0pt}
+  \setlength{\@fpsep}{8pt plus 2.0fil}
+  \setlength{\@fpbot}{0pt plus 1.0fil}
+\makeatother
+
+% rules for floats:
+\newcommand{\topfigrule}{\vspace*{10pt}{\hrule height0.4pt}\vspace*{-10.4pt}}
+\newcommand{\bottomfigrule}{\vspace*{-10.4pt}{\hrule height0.4pt}\vspace*{10pt}}
+
+% captions:
+\usepackage[format=plain,singlelinecheck=off,labelfont=bf,font={small,sf}]{caption}
+
+% put caption on separate float:
+\newcommand{\breakfloat}{\end{figure}\begin{figure}[t]}
+
+% references to panels of a figure within the caption:
+\newcommand{\figitem}[1]{\textsf{\bfseries\uppercase{#1}}}
+% references to figures:
+\newcommand{\panel}[1]{\textsf{\uppercase{#1}}}
+\newcommand{\fref}[1]{\textup{\ref{#1}}}
+\newcommand{\subfref}[2]{\textup{\ref{#1}}\,\panel{#2}}
+% references to figures in normal text:
+\newcommand{\fig}{Fig.}
+\newcommand{\Fig}{Figure}
+\newcommand{\figs}{Figs.}
+\newcommand{\Figs}{Figures}
+\newcommand{\figref}[1]{\fig~\fref{#1}}
+\newcommand{\Figref}[1]{\Fig~\fref{#1}}
+\newcommand{\figsref}[1]{\figs~\fref{#1}}
+\newcommand{\Figsref}[1]{\Figs~\fref{#1}}
+\newcommand{\subfigref}[2]{\fig~\subfref{#1}{#2}}
+\newcommand{\Subfigref}[2]{\Fig~\subfref{#1}{#2}}
+\newcommand{\subfigsref}[2]{\figs~\subfref{#1}{#2}}
+\newcommand{\Subfigsref}[2]{\Figs~\subfref{#1}{#2}}
+% references to figures within bracketed text:
+\newcommand{\figb}{Fig.}
+\newcommand{\figsb}{Figs.}
+\newcommand{\figrefb}[1]{\figb~\fref{#1}}
+\newcommand{\figsrefb}[1]{\figsb~\fref{#1}}
+\newcommand{\subfigrefb}[2]{\figb~\subfref{#1}{#2}}
+\newcommand{\subfigsrefb}[2]{\figsb~\subfref{#1}{#2}}
+
+% references to tables:
+\newcommand{\tref}[1]{\textup{\ref{#1}}}
+% references to tables in normal text:
+\newcommand{\tab}{Tab.}
+\newcommand{\Tab}{Table}
+\newcommand{\tabs}{Tabs.}
+\newcommand{\Tabs}{Tables}
+\newcommand{\tabref}[1]{\tab~\tref{#1}}
+\newcommand{\Tabref}[1]{\Tab~\tref{#1}}
+\newcommand{\tabsref}[1]{\tabs~\tref{#1}}
+\newcommand{\Tabsref}[1]{\Tabs~\tref{#1}}
+% references to tables within bracketed text:
+\newcommand{\tabb}{Tab.}
+\newcommand{\tabsb}{Tab.}
+\newcommand{\tabrefb}[1]{\tabb~\tref{#1}}
+\newcommand{\tabsrefb}[1]{\tabsb~\tref{#1}}
+
+
+%%%%% equation references %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%\newcommand{\eqref}[1]{(\ref{#1})}
+\newcommand{\eqn}{\tr{Eq}{Gl}.}
+\newcommand{\Eqn}{\tr{Eq}{Gl}.}
+\newcommand{\eqns}{\tr{Eqs}{Gln}.}
+\newcommand{\Eqns}{\tr{Eqs}{Gln}.}
+\newcommand{\eqnref}[1]{\eqn~\eqref{#1}}
+\newcommand{\Eqnref}[1]{\Eqn~\eqref{#1}}
+\newcommand{\eqnsref}[1]{\eqns~\eqref{#1}}
+\newcommand{\Eqnsref}[1]{\Eqns~\eqref{#1}}
+
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+  inputpath=../code,
+  basicstyle=\ttfamily\footnotesize,
+  numbers=left,
+  showstringspaces=false,
+  language=Matlab,
+  commentstyle=\itshape\color{darkgray},
+  keywordstyle=\color{blue},
+  stringstyle=\color{green},
+  backgroundcolor=\color{blue!10},
+  breaklines=true,
+  breakautoindent=true,
+  columns=flexible,
+  frame=single,
+  caption={\protect\filename@parse{\lstname}\protect\filename@base},
+  captionpos=t,
+  xleftmargin=1em,
+  xrightmargin=1em,
+  aboveskip=10pt
+}
+
+%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{amsmath}
+\usepackage{bm} 
+\usepackage{dsfont}
+\newcommand{\naZ}{\mathds{N}}
+\newcommand{\gaZ}{\mathds{Z}}
+\newcommand{\raZ}{\mathds{Q}}
+\newcommand{\reZ}{\mathds{R}}
+\newcommand{\reZp}{\mathds{R^+}}
+\newcommand{\reZpN}{\mathds{R^+_0}}
+\newcommand{\koZ}{\mathds{C}}
+
+
+%%%%% structure: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{ifthen}
+
+\newcommand{\code}[1]{\texttt{#1}}
+
+\newcommand{\source}[1]{    
+  \begin{flushright}
+    \color{gray}\scriptsize \url{#1}
+  \end{flushright}
+}
+
+\newenvironment{definition}[1][]{\medskip\noindent\textbf{Definition}\ifthenelse{\equal{#1}{}}{}{ #1}:\newline}%
+  {\medskip}
+
+\newcounter{maxexercise} 
+\setcounter{maxexercise}{9}  % show listings up to exercise maxexercise
+\newcounter{theexercise} 
+\setcounter{theexercise}{1}
+\newenvironment{exercise}[1][]{\medskip\noindent\textbf{\tr{Exercise}{\"Ubung}
+  \arabic{theexercise}:}\newline \newcommand{\exercisesource}{#1}}%
+  {\ifthenelse{\equal{\exercisesource}{}}{}{\ifthenelse{\value{theexercise}>\value{maxexercise}}{}{\medskip\lstinputlisting{\exercisesource}}}\medskip\stepcounter{theexercise}}
+
+\graphicspath{{figures/}}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document} 
+
+\include{pointprocesses}
+
+\end{document}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{\tr{Homogeneous Poisson process}{Homogener Poisson Prozess}}
+
+\begin{figure}[t]
+  \includegraphics[width=1\textwidth]{poissonraster100hz}
+  \caption{\label{hompoissonfig}Rasterplot von Poisson-Spikes.}
+\end{figure}
+
+The probability $p(t)\delta t$ of an event occuring at time $t$
+is independent of $t$ and independent of any previous event
+(independent of event history).
+
+The probability $P$ for an event occuring within a time bin of width $\Delta t$
+is
+\[ P=\lambda \cdot \Delta t \]
+for a Poisson process with rate $\lambda$.
+
+\subsection{Statistics of homogeneous Poisson process}
+
+\begin{figure}[t]
+  \includegraphics[width=0.45\textwidth]{poissonisihexp20hz}\hfill
+  \includegraphics[width=0.45\textwidth]{poissonisihexp100hz}
+  \caption{\label{hompoissonisihfig}Interspike interval histograms of poisson spike train.}
+\end{figure}
+
+\begin{itemize}
+\item Exponential distribution of intervals $T$: $p(T) = \lambda e^{-\lambda T}$
+\item Mean interval $\mu_{ISI} = \frac{1}{\lambda}$
+\item Variance of intervals $\sigma_{ISI}^2 = \frac{1}{\lambda^2}$
+\item Coefficient of variation $CV_{ISI} = 1$
+\item Serial correlation $\rho_k =0$ for $k>0$ (renewal process!)   
+\item Fano factor $F=1$
+\end{itemize}
+
+\subsection{Count statistics of Poisson process}
+
+\begin{figure}[t]
+  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}\hfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}
+  \caption{\label{hompoissoncountfig}Count statistics of poisson spike train.}
+\end{figure}
+
+Poisson distribution:
+\[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
\ No newline at end of file
diff --git a/pointprocesses/lecture/pointprocesses-slides.tex b/pointprocesses/lecture/pointprocesses-slides.tex
new file mode 100644
index 0000000..ff49cb2
--- /dev/null
+++ b/pointprocesses/lecture/pointprocesses-slides.tex
@@ -0,0 +1,412 @@
+\documentclass{beamer}
+
+%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\title[]{Scientific Computing --- Point Processes}
+\author[]{Jan Benda}
+\institute[]{Neuroethology}
+\date[]{WS 14/15}
+\titlegraphic{\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
+
+%%%%% beamer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\mode<presentation>
+{
+  \usetheme{Singapore}
+  \setbeamercovered{opaque}
+  \usecolortheme{tuebingen}
+  \setbeamertemplate{navigation symbols}{}
+  \usefonttheme{default}
+  \useoutertheme{infolines}
+  % \useoutertheme{miniframes}
+}
+
+%\AtBeginSection[]
+%{
+%  \begin{frame}<beamer>
+%    \begin{center}
+%      \Huge \insertsectionhead
+%    \end{center}
+%  \end{frame}
+%}
+
+\setbeamertemplate{blocks}[rounded][shadow=true]
+\setcounter{tocdepth}{1}
+
+%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[english]{babel}
+\usepackage{amsmath}
+\usepackage{bm} 
+\usepackage{pslatex}   % nice font for pdf file
+%\usepackage{multimedia}
+
+\usepackage{dsfont}
+\newcommand{\naZ}{\mathds{N}}
+\newcommand{\gaZ}{\mathds{Z}}
+\newcommand{\raZ}{\mathds{Q}}
+\newcommand{\reZ}{\mathds{R}}
+\newcommand{\reZp}{\mathds{R^+}}
+\newcommand{\reZpN}{\mathds{R^+_0}}
+\newcommand{\koZ}{\mathds{C}}
+
+%%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{graphicx}
+\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+ basicstyle=\ttfamily,
+ numbers=left,
+ showstringspaces=false,
+ language=Matlab,
+ commentstyle=\itshape\color{darkgray},
+ keywordstyle=\color{blue},
+ stringstyle=\color{green},
+ backgroundcolor=\color{blue!10},
+ breaklines=true,
+ breakautoindent=true,
+ columns=flexible,
+ frame=single,
+ captionpos=b,
+ xleftmargin=1em,
+ xrightmargin=1em,
+ aboveskip=10pt
+ }
+
+ 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document} 
+
+\begin{frame}[plain]
+  \frametitle{}
+  \vspace{-1cm}
+  \titlepage % erzeugt Titelseite
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Content}
+  \tableofcontents
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Point processes}
+
+\begin{frame}
+  \frametitle{Point process}
+  \vspace{-3ex}
+  \texpicture{pointprocessscetchA}
+
+  A point process is a stochastic (or random) process that generates a sequence of events
+  at times $\{t_i\}$, $t_i \in \reZ$.
+
+  For each point process there is an underlying continuous-valued
+  process evolving in time. The associated point process occurs when
+  the underlying continuous process crosses a threshold.
+  Examples:
+  \begin{itemize}
+  \item Spikes/heartbeat: generated by the dynamics of the membrane potential of neurons/heart cells.
+  \item Earth quakes: generated by the pressure dynamics between the tectonic plates on either side of a geological fault line.
+  \item Onset of cricket/frogs/birds/... songs: generated by the dynamics of the state of a nervous system.
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Point process}
+  \texpicture{pointprocessscetchB}
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Homogeneous Poisson process}
+
+\begin{frame}
+  \frametitle{Homogeneous Poisson process}
+  The probability $p(t)\delta t$ of an event occuring at time $t$
+  is independent of $t$ and independent of any previous event
+  (independent of event history).
+
+  The probability $P$ for an event occuring within a time bin of width $\Delta t$
+  is
+  \[ P=\lambda \cdot \Delta t \]
+  for a Poisson process with rate $\lambda$.
+  \includegraphics[width=1\textwidth]{poissonraster100hz}
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Interval statistics}
+
+\begin{frame}
+  \frametitle{Rate}
+  Rate of events $r$ (``spikes per time'') measured in Hertz.
+  \begin{itemize}
+  \item Number of events $N$ per observation time $W$: $r = \frac{N}{W}$
+  \item Without boundary effects: $r = \frac{N-1}{t_N-t_1}$
+  \item Inverse interval: $r = \frac{1}{\mu_{ISI}}$
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{(Interspike) interval statistics}
+  \begin{itemize}
+  \item Histogram $p(T)$ of intervals $T$. Normalized to $\int_0^{\infty} p(T) \; dT = 1$
+  \item Mean interval $\mu_{ISI} = \langle T \rangle = \frac{1}{n}\sum\limits_{i=1}^n T_i$
+  \item Variance of intervals $\sigma_{ISI}^2 = \langle (T - \langle T \rangle)^2 \rangle$\vspace{1ex}
+  \item Coefficient of variation $CV_{ISI} = \frac{\sigma_{ISI}}{\mu_{ISI}}$
+  \item Diffusion coefficient $D_{ISI} = \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$
+  \vfill
+  \end{itemize}
+  \includegraphics[width=0.45\textwidth]{poissonisih100hz}\hfill
+  \includegraphics[width=0.45\textwidth]{lifisih16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval statistics of homogeneous Poisson process}
+  \begin{itemize}
+  \item Exponential distribution of intervals $T$: $p(T) = \lambda e^{-\lambda T}$
+  \item Mean interval $\mu_{ISI} = \frac{1}{\lambda}$
+  \item Variance of intervals $\sigma_{ISI}^2 = \frac{1}{\lambda^2}$
+  \item Coefficient of variation $CV_{ISI} = 1$
+  \end{itemize}
+  \vfill
+  \includegraphics[width=0.45\textwidth]{poissonisihexp20hz}\hfill
+  \includegraphics[width=0.45\textwidth]{poissonisihexp100hz}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return maps}
+  Scatter plot between succeeding intervals separated by lag $k$.
+  \vfill
+  Poisson process $\lambda=100$\,Hz:
+  \includegraphics[width=1\textwidth]{poissonreturnmap100hz}\hfill
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial interval correlations}
+  Correlation coefficients between succeeding intervals separated by lag $k$:
+  \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
+  \begin{itemize}
+  \item $\rho_0=1$ (correlation of each interval with itself).
+  \item Poisson process: $\rho_k =0$ for $k>0$ (renewal process!) 
+  \end{itemize}
+  \vfill
+  \includegraphics[width=0.7\textwidth]{poissonserial100hz}
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Count statistics}
+
+\begin{frame}
+  \frametitle{Count statistics}
+  Histogram of number of events $N$ (counts) within observation window of duration $W$.
+
+  \vfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthist100hz10ms}\hfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthist100hz100ms}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of Poisson process}
+  Poisson distribution:
+  \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
+
+  \vfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}\hfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics --- Fano factor}
+  Statistics of number of events $N$ within observation window of duration $W$.
+  \begin{itemize}
+  \item Mean count: $\mu_N = \langle N \rangle$
+  \item Count variance: $\sigma_N^2 = \langle (N - \langle N \rangle)^2 \rangle$
+  \item Fano factor (variance divided by mean): $F = \frac{\sigma_N^2}{\mu_N}$
+  \item Poisson process: $F=1$
+  \end{itemize}
+  \vfill
+  Poisson process $\lambda=100$\,Hz:
+  \includegraphics[width=1\textwidth]{poissonfano100hz}
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Integrate-and-fire models}
+
+\begin{frame}
+  \frametitle{Integrate-and-fire models}
+  Leaky integrate-and-fire model (LIF):
+  \[ \tau \frac{dV}{dt} = -V + RI + D\xi \]
+  Whenever membrane potential $V(t)$ crosses the firing threshold $\theta$, a spike is emitted and
+  $V(t)$ is reset to $V_{reset}$.
+  \begin{itemize}
+  \item $\tau$: membrane time constant (typically 10\,ms)
+  \item $R$: input resistance (here 1\,mV (!))
+  \item $D\xi$: additive Gaussian white noise of strength $D$
+  \item $\theta$: firing threshold (here 10\,mV)
+  \item $V_{reset}$: reset potential (here 0\,mV)
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Integrate-and-fire models}
+  Discretization with time step $\Delta t$: $V(t) \rightarrow V_i,\;t_i = i \Delta t$.\\
+  Euler integration:
+  \begin{eqnarray*}
+    \frac{dV}{dt} & \approx & \frac{V_{i+1} - V_i}{\Delta t} \\
+    \Rightarrow \quad V_{i+1} & = & V_i + \Delta t \frac{-V_i+RI_i+\sqrt{2D\Delta t}N_i}{\tau}
+  \end{eqnarray*}
+  $N_i$ are normally distributed random numbers (Gaussian with zero mean and unit variance)
+  --- the $\sqrt{\Delta t}$ is for white noise.
+
+  \includegraphics[width=0.82\textwidth]{lifraster16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval statistics of LIF}
+  Interval distribution approaches Inverse Gaussian for large $I$:
+  \[ p(T) = \frac{1}{\sqrt{4\pi D T^3}}\exp\left[-\frac{(T-\langle T \rangle)^2}{4DT\langle T \rangle^2}\right] \]
+  where $\langle T \rangle$ is the mean interspike interval and $D$
+  is the diffusion coefficient.
+  \vfill
+  \includegraphics[width=0.45\textwidth]{lifisihdistr08}\hfill
+  \includegraphics[width=0.45\textwidth]{lifisihdistr16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval statistics of PIF}
+  For the perfect integrate-and-fire (PIF)
+  \[ \tau \frac{dV}{dt} =  RI + D\xi \]
+  (the canonical model or supra-threshold firing on a limit cycle)\\
+  the Inverse Gaussian describes exactly the interspike interval distribution.
+  \vfill
+  \includegraphics[width=0.45\textwidth]{pifisihdistr01}\hfill
+  \includegraphics[width=0.45\textwidth]{pifisihdistr10}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return map of LIF}
+  LIF $I=15.7$:
+  \includegraphics[width=1\textwidth]{lifreturnmap16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial correlations of LIF}
+  LIF $I=15.7$:
+  \includegraphics[width=1\textwidth]{lifserial16}\\
+  Integrate-and-fire driven with white noise are still renewal processes!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of LIF}
+  LIF $I=15.7$:
+  \includegraphics[width=1\textwidth]{liffano16}\\
+  Fano factor is not one!
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Interval statistics of LIF with OU noise}
+  \begin{eqnarray*}
+    \tau \frac{dV}{dt} & = & -V + RI + U \\
+    \tau_{OU} \frac{dU}{dt} & = & - U + D\xi
+  \end{eqnarray*}
+  Ohrnstein-Uhlenbeck noise is lowpass filtered white noise.
+  \includegraphics[width=0.45\textwidth]{lifouisihdistr08-100ms}\hfill
+  \includegraphics[width=0.45\textwidth]{lifouisihdistr16-100ms}\\
+  More peaky than the inverse Gaussian!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return map of LIF with OU noise}
+  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifoureturnmap16-100ms}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial correlations of LIF with OU noise}
+  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifouserial16-100ms}\\
+  OU-noise introduces positive interval correlations!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of LIF with OU noise}
+  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifoufano16-100ms}\\
+  Fano factor increases with count window duration.
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Interval statistics of LIF with adaptation}
+  \begin{eqnarray*}
+    \tau \frac{dV}{dt} & = & -V - A + RI + D\xi \\
+    \tau_{adapt} \frac{dA}{dt} & = & - A
+  \end{eqnarray*}
+  Adaptation $A$ with time constant $\tau_{adapt}$ and increment $\Delta A$ at spike.
+  \includegraphics[width=0.45\textwidth]{lifadaptisihdistr08-100ms}\hfill
+  \includegraphics[width=0.45\textwidth]{lifadaptisihdistr65-100ms}\\
+  Similar to LIF with white noise.
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return map of LIF with adaptation}
+  LIF $I=10$, $\tau_{adapt}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifadaptreturnmap10-100ms}\\
+  Negative correlation at lag one.
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial correlations of LIF with adaptation}
+  LIF $I=10$, $\tau_{adapt}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifadaptserial10-100ms}\\
+  Adaptation with white noise introduces negative interval correlations!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of LIF with adaptation}
+  LIF $I=10$, $\tau_{adapt}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifadaptfano10-100ms}\\
+  Fano factor decreases with count window duration.
+\end{frame}
+
+
+\end{document}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Non stationary}
+\subsection{Inhomogeneous Poisson process}
+\subsection{Firing rate}
+\subsection{Instantaneous rate}
+\subsection{Autocorrelation}
+\subsection{Crosscorrelation}
+\subsection{Joint PSTH}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Renewal process}
+\subsection{Superthreshold firing}
+\subsection{Subthreshold firing}
+\section{Non-renewal processes}
+\subsection{Bursting}
+\subsection{Resonator}
+
+
+\subsection{Standard distributions}
+\subsubsection{Gamma}
+\subsubsection{How to read ISI histograms}
+refractoriness, poisson tail, sub-, supra-threshold, missed spikes
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Correlation with stimulus}
+\subsection{Tuning curve}
+\subsection{Linear filter}
+\subsection{Spatiotemporal receptive field}
+\subsection{Generalized linear model}
+
+\begin{frame}
+\end{frame}
diff --git a/pointprocesses/lecture/pointprocesses.tex b/pointprocesses/lecture/pointprocesses.tex
index ff49cb2..b654a20 100644
--- a/pointprocesses/lecture/pointprocesses.tex
+++ b/pointprocesses/lecture/pointprocesses.tex
@@ -1,412 +1,107 @@
-\documentclass{beamer}
-
-%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\title[]{Scientific Computing --- Point Processes}
-\author[]{Jan Benda}
-\institute[]{Neuroethology}
-\date[]{WS 14/15}
-\titlegraphic{\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
-
-%%%%% beamer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\mode<presentation>
-{
-  \usetheme{Singapore}
-  \setbeamercovered{opaque}
-  \usecolortheme{tuebingen}
-  \setbeamertemplate{navigation symbols}{}
-  \usefonttheme{default}
-  \useoutertheme{infolines}
-  % \useoutertheme{miniframes}
-}
-
-%\AtBeginSection[]
-%{
-%  \begin{frame}<beamer>
-%    \begin{center}
-%      \Huge \insertsectionhead
-%    \end{center}
-%  \end{frame}
-%}
-
-\setbeamertemplate{blocks}[rounded][shadow=true]
-\setcounter{tocdepth}{1}
-
-%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[english]{babel}
-\usepackage{amsmath}
-\usepackage{bm} 
-\usepackage{pslatex}   % nice font for pdf file
-%\usepackage{multimedia}
-
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{graphicx}
-\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
- basicstyle=\ttfamily,
- numbers=left,
- showstringspaces=false,
- language=Matlab,
- commentstyle=\itshape\color{darkgray},
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- }
-
- 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document} 
-
-\begin{frame}[plain]
-  \frametitle{}
-  \vspace{-1cm}
-  \titlepage % erzeugt Titelseite
-\end{frame}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{frame}
-  \frametitle{Content}
-  \tableofcontents
-\end{frame}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Point processes}
-
-\begin{frame}
-  \frametitle{Point process}
-  \vspace{-3ex}
-  \texpicture{pointprocessscetchA}
-
-  A point process is a stochastic (or random) process that generates a sequence of events
-  at times $\{t_i\}$, $t_i \in \reZ$.
-
-  For each point process there is an underlying continuous-valued
-  process evolving in time. The associated point process occurs when
-  the underlying continuous process crosses a threshold.
-  Examples:
-  \begin{itemize}
-  \item Spikes/heartbeat: generated by the dynamics of the membrane potential of neurons/heart cells.
-  \item Earth quakes: generated by the pressure dynamics between the tectonic plates on either side of a geological fault line.
-  \item Onset of cricket/frogs/birds/... songs: generated by the dynamics of the state of a nervous system.
-  \end{itemize}
-\end{frame}
+\chapter{\tr{Point processes}{Punktprozesse}}
 
-\begin{frame}
-  \frametitle{Point process}
+\begin{figure}[t]
   \texpicture{pointprocessscetchB}
-\end{frame}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Homogeneous Poisson process}
-
-\begin{frame}
-  \frametitle{Homogeneous Poisson process}
-  The probability $p(t)\delta t$ of an event occuring at time $t$
-  is independent of $t$ and independent of any previous event
-  (independent of event history).
-
-  The probability $P$ for an event occuring within a time bin of width $\Delta t$
-  is
-  \[ P=\lambda \cdot \Delta t \]
-  for a Poisson process with rate $\lambda$.
-  \includegraphics[width=1\textwidth]{poissonraster100hz}
-\end{frame}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Interval statistics}
-
-\begin{frame}
-  \frametitle{Rate}
-  Rate of events $r$ (``spikes per time'') measured in Hertz.
-  \begin{itemize}
-  \item Number of events $N$ per observation time $W$: $r = \frac{N}{W}$
-  \item Without boundary effects: $r = \frac{N-1}{t_N-t_1}$
-  \item Inverse interval: $r = \frac{1}{\mu_{ISI}}$
-  \end{itemize}
-\end{frame}
-
-\begin{frame}
-  \frametitle{(Interspike) interval statistics}
-  \begin{itemize}
-  \item Histogram $p(T)$ of intervals $T$. Normalized to $\int_0^{\infty} p(T) \; dT = 1$
-  \item Mean interval $\mu_{ISI} = \langle T \rangle = \frac{1}{n}\sum\limits_{i=1}^n T_i$
-  \item Variance of intervals $\sigma_{ISI}^2 = \langle (T - \langle T \rangle)^2 \rangle$\vspace{1ex}
-  \item Coefficient of variation $CV_{ISI} = \frac{\sigma_{ISI}}{\mu_{ISI}}$
-  \item Diffusion coefficient $D_{ISI} = \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$
-  \vfill
-  \end{itemize}
-  \includegraphics[width=0.45\textwidth]{poissonisih100hz}\hfill
-  \includegraphics[width=0.45\textwidth]{lifisih16}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval statistics of homogeneous Poisson process}
-  \begin{itemize}
-  \item Exponential distribution of intervals $T$: $p(T) = \lambda e^{-\lambda T}$
-  \item Mean interval $\mu_{ISI} = \frac{1}{\lambda}$
-  \item Variance of intervals $\sigma_{ISI}^2 = \frac{1}{\lambda^2}$
-  \item Coefficient of variation $CV_{ISI} = 1$
-  \end{itemize}
-  \vfill
-  \includegraphics[width=0.45\textwidth]{poissonisihexp20hz}\hfill
-  \includegraphics[width=0.45\textwidth]{poissonisihexp100hz}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval return maps}
-  Scatter plot between succeeding intervals separated by lag $k$.
-  \vfill
-  Poisson process $\lambda=100$\,Hz:
-  \includegraphics[width=1\textwidth]{poissonreturnmap100hz}\hfill
-\end{frame}
-
-\begin{frame}
-  \frametitle{Serial interval correlations}
-  Correlation coefficients between succeeding intervals separated by lag $k$:
-  \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
-  \begin{itemize}
-  \item $\rho_0=1$ (correlation of each interval with itself).
-  \item Poisson process: $\rho_k =0$ for $k>0$ (renewal process!) 
-  \end{itemize}
-  \vfill
-  \includegraphics[width=0.7\textwidth]{poissonserial100hz}
-\end{frame}
+  \caption{\label{pointprocessscetchfig}Ein Punktprozess ist eine
+    Abfolge von Zeitpunkten $t_i$ die auch durch die Intervalle
+    $T_i=t_{i+1}-t_i$ oder die Anzahl der Ereignisse $n_i$ beschrieben
+    werden kann. }
+\end{figure}
+
+Ein zeitlicher Punktprozess ist ein stochastischer Prozess, der eine
+Abfolge von Ereignissen zu den Zeiten $\{t_i\}$, $t_i \in \reZ$,
+generiert.
+
+Jeder Punktprozess wird durch einen sich in der Zeit kontinuierlich
+entwickelnden Prozess generiert. Wann immer dieser Prozess eine
+Schwelle \"uberschreitet wird ein Ereigniss des Punktprozesses
+erzeugt. Zum Beispiel:
+\begin{itemize}
+\item Aktionspotentiale/Herzschlag: wird durch die Dynamik des
+  Membranpotentials eines Neurons/Herzzelle erzeugt.
+\item Erdbeben: wird durch die Dynamik des Druckes zwischen
+  tektonischen Platten auf beiden Seiten einer geologischen Verwerfung
+  erzeugt.
+\item Zeitpunkt eines Grillen/Frosch/Vogelgesangs: wird durch die
+  Dynamik des Nervensystems und des Muskelapparates erzeugt.
+\end{itemize}
+
+\begin{figure}[t]
+  \includegraphics[width=1\textwidth]{rasterexamples}
+  \caption{\label{rasterexamplesfig}Raster-Plot von jeweils 10
+    Realisierungen eines station\"arenen Punktprozesses (homogener
+    Poisson Prozess mit Rate $\lambda=20$\;Hz, links) und eines
+    nicht-station\"aren Punktprozesses (perfect integrate-and-fire
+    Neuron getrieben mit Ohrnstein-Uhlenbeck Rauschen mit
+    Zeitkonstante $\tau=100$\,ms, rechts).}
+\end{figure}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+\section{Intervall Statistik}
+
+\begin{figure}[t]
+  \includegraphics[width=1\textwidth]{isihexamples}\hfill
+  \caption{\label{isihexamplesfig}Interspike-Intervall Histogramme der in
+    \figref{rasterexamplesfig} gezeigten Spikes.}
+\end{figure}
+
+\subsection{(Interspike) Intervall Statistik erster Ordnung}
+\begin{itemize}
+\item Histogramm $p(T)$ der Intervalle $T$. Normiert auf $\int_0^{\infty} p(T) \; dT = 1$.
+\item Mittleres Intervall $\mu_{ISI} = \langle T \rangle = \frac{1}{n}\sum\limits_{i=1}^n T_i$.
+\item Varianz der Intervalle $\sigma_{ISI}^2 = \langle (T - \langle T \rangle)^2 \rangle$\vspace{1ex}
+\item Variationskoeffizient (``Coefficient of variation'') $CV_{ISI} = \frac{\sigma_{ISI}}{\mu_{ISI}}$.
+\item Diffusions Koeffizient $D_{ISI} = \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$.
+\end{itemize}
+
+\subsection{Interval return maps}
+Scatter plot von aufeinander folgenden Intervallen $(T_{i+k}, T_i)$ getrennt durch das ``lag'' $k$.
+
+\begin{figure}[t]
+  \includegraphics[width=1\textwidth]{returnmapexamples}
+  \includegraphics[width=1\textwidth]{serialcorrexamples}
+  \caption{\label{returnmapfig}Interspike-Intervall return maps and serial correlations.}
+\end{figure}
+
+\subsection{Serielle Korrelationen der Intervalle}
+Korrelationskoeffizient zwischen aufeinander folgenden Intervallen getrennt durch ``lag'' $k$:
+\[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
+$\rho_0=1$ (Korrelation jedes Intervalls mit sich selber).
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Count statistics}
-
-\begin{frame}
-  \frametitle{Count statistics}
-  Histogram of number of events $N$ (counts) within observation window of duration $W$.
+\section{Z\"ahlstatistik}
 
-  \vfill
+\begin{figure}[t]
   \includegraphics[width=0.48\textwidth]{poissoncounthist100hz10ms}\hfill
   \includegraphics[width=0.48\textwidth]{poissoncounthist100hz100ms}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Count statistics of Poisson process}
-  Poisson distribution:
-  \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
-
-  \vfill
-  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}\hfill
-  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Count statistics --- Fano factor}
-  Statistics of number of events $N$ within observation window of duration $W$.
-  \begin{itemize}
-  \item Mean count: $\mu_N = \langle N \rangle$
-  \item Count variance: $\sigma_N^2 = \langle (N - \langle N \rangle)^2 \rangle$
-  \item Fano factor (variance divided by mean): $F = \frac{\sigma_N^2}{\mu_N}$
-  \item Poisson process: $F=1$
-  \end{itemize}
-  \vfill
-  Poisson process $\lambda=100$\,Hz:
-  \includegraphics[width=1\textwidth]{poissonfano100hz}
-\end{frame}
-
+  \caption{\label{countstatsfig}Count Statistik.}
+\end{figure}
+
+Statistik der Anzahl der Ereignisse $N_i$ innerhalb von Beobachtungsfenstern $i$ der Breite $W$.
+\begin{itemize}
+\item Histogramm der counts $N_i$.
+\item Mittlere Anzahl von Ereignissen: $\mu_N = \langle N \rangle$.
+\item Varianz der Anzahl: $\sigma_N^2 = \langle (N - \langle N \rangle)^2 \rangle$.
+\item Fano Faktor (Varianz geteilt durch Mittelwert): $F = \frac{\sigma_N^2}{\mu_N}$.
+\end{itemize}
+
+Insbesondere ist die mittlere Rate der Ereignisse $r$ (``Spikes pro Zeit'', Feuerrate) gemessen in Hertz
+\[ r = \frac{\langle N \rangle}{W} \; . \]
+
+\begin{figure}[t]
+  \begin{minipage}[t]{0.49\textwidth}
+    Poisson process $\lambda=100$\,Hz:\\
+    \includegraphics[width=1\textwidth]{poissonfano100hz}
+  \end{minipage}
+  \hfill
+  \begin{minipage}[t]{0.49\textwidth}
+    LIF $I=10$, $\tau_{adapt}=100$\,ms:\\
+    \includegraphics[width=1\textwidth]{lifadaptfano10-100ms}
+  \end{minipage}
+  \caption{\label{fanofig}Fano factor.}
+\end{figure}
 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Integrate-and-fire models}
-
-\begin{frame}
-  \frametitle{Integrate-and-fire models}
-  Leaky integrate-and-fire model (LIF):
-  \[ \tau \frac{dV}{dt} = -V + RI + D\xi \]
-  Whenever membrane potential $V(t)$ crosses the firing threshold $\theta$, a spike is emitted and
-  $V(t)$ is reset to $V_{reset}$.
-  \begin{itemize}
-  \item $\tau$: membrane time constant (typically 10\,ms)
-  \item $R$: input resistance (here 1\,mV (!))
-  \item $D\xi$: additive Gaussian white noise of strength $D$
-  \item $\theta$: firing threshold (here 10\,mV)
-  \item $V_{reset}$: reset potential (here 0\,mV)
-  \end{itemize}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Integrate-and-fire models}
-  Discretization with time step $\Delta t$: $V(t) \rightarrow V_i,\;t_i = i \Delta t$.\\
-  Euler integration:
-  \begin{eqnarray*}
-    \frac{dV}{dt} & \approx & \frac{V_{i+1} - V_i}{\Delta t} \\
-    \Rightarrow \quad V_{i+1} & = & V_i + \Delta t \frac{-V_i+RI_i+\sqrt{2D\Delta t}N_i}{\tau}
-  \end{eqnarray*}
-  $N_i$ are normally distributed random numbers (Gaussian with zero mean and unit variance)
-  --- the $\sqrt{\Delta t}$ is for white noise.
-
-  \includegraphics[width=0.82\textwidth]{lifraster16}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval statistics of LIF}
-  Interval distribution approaches Inverse Gaussian for large $I$:
-  \[ p(T) = \frac{1}{\sqrt{4\pi D T^3}}\exp\left[-\frac{(T-\langle T \rangle)^2}{4DT\langle T \rangle^2}\right] \]
-  where $\langle T \rangle$ is the mean interspike interval and $D$
-  is the diffusion coefficient.
-  \vfill
-  \includegraphics[width=0.45\textwidth]{lifisihdistr08}\hfill
-  \includegraphics[width=0.45\textwidth]{lifisihdistr16}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval statistics of PIF}
-  For the perfect integrate-and-fire (PIF)
-  \[ \tau \frac{dV}{dt} =  RI + D\xi \]
-  (the canonical model or supra-threshold firing on a limit cycle)\\
-  the Inverse Gaussian describes exactly the interspike interval distribution.
-  \vfill
-  \includegraphics[width=0.45\textwidth]{pifisihdistr01}\hfill
-  \includegraphics[width=0.45\textwidth]{pifisihdistr10}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval return map of LIF}
-  LIF $I=15.7$:
-  \includegraphics[width=1\textwidth]{lifreturnmap16}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Serial correlations of LIF}
-  LIF $I=15.7$:
-  \includegraphics[width=1\textwidth]{lifserial16}\\
-  Integrate-and-fire driven with white noise are still renewal processes!
-\end{frame}
-
-\begin{frame}
-  \frametitle{Count statistics of LIF}
-  LIF $I=15.7$:
-  \includegraphics[width=1\textwidth]{liffano16}\\
-  Fano factor is not one!
-\end{frame}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{frame}
-  \frametitle{Interval statistics of LIF with OU noise}
-  \begin{eqnarray*}
-    \tau \frac{dV}{dt} & = & -V + RI + U \\
-    \tau_{OU} \frac{dU}{dt} & = & - U + D\xi
-  \end{eqnarray*}
-  Ohrnstein-Uhlenbeck noise is lowpass filtered white noise.
-  \includegraphics[width=0.45\textwidth]{lifouisihdistr08-100ms}\hfill
-  \includegraphics[width=0.45\textwidth]{lifouisihdistr16-100ms}\\
-  More peaky than the inverse Gaussian!
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval return map of LIF with OU noise}
-  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
-  \includegraphics[width=1\textwidth]{lifoureturnmap16-100ms}
-\end{frame}
-
-\begin{frame}
-  \frametitle{Serial correlations of LIF with OU noise}
-  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
-  \includegraphics[width=1\textwidth]{lifouserial16-100ms}\\
-  OU-noise introduces positive interval correlations!
-\end{frame}
-
-\begin{frame}
-  \frametitle{Count statistics of LIF with OU noise}
-  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
-  \includegraphics[width=1\textwidth]{lifoufano16-100ms}\\
-  Fano factor increases with count window duration.
-\end{frame}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{frame}
-  \frametitle{Interval statistics of LIF with adaptation}
-  \begin{eqnarray*}
-    \tau \frac{dV}{dt} & = & -V - A + RI + D\xi \\
-    \tau_{adapt} \frac{dA}{dt} & = & - A
-  \end{eqnarray*}
-  Adaptation $A$ with time constant $\tau_{adapt}$ and increment $\Delta A$ at spike.
-  \includegraphics[width=0.45\textwidth]{lifadaptisihdistr08-100ms}\hfill
-  \includegraphics[width=0.45\textwidth]{lifadaptisihdistr65-100ms}\\
-  Similar to LIF with white noise.
-\end{frame}
-
-\begin{frame}
-  \frametitle{Interval return map of LIF with adaptation}
-  LIF $I=10$, $\tau_{adapt}=100$\,ms:
-  \includegraphics[width=1\textwidth]{lifadaptreturnmap10-100ms}\\
-  Negative correlation at lag one.
-\end{frame}
-
-\begin{frame}
-  \frametitle{Serial correlations of LIF with adaptation}
-  LIF $I=10$, $\tau_{adapt}=100$\,ms:
-  \includegraphics[width=1\textwidth]{lifadaptserial10-100ms}\\
-  Adaptation with white noise introduces negative interval correlations!
-\end{frame}
-
-\begin{frame}
-  \frametitle{Count statistics of LIF with adaptation}
-  LIF $I=10$, $\tau_{adapt}=100$\,ms:
-  \includegraphics[width=1\textwidth]{lifadaptfano10-100ms}\\
-  Fano factor decreases with count window duration.
-\end{frame}
-
-
-\end{document}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Non stationary}
-\subsection{Inhomogeneous Poisson process}
-\subsection{Firing rate}
-\subsection{Instantaneous rate}
-\subsection{Autocorrelation}
-\subsection{Crosscorrelation}
-\subsection{Joint PSTH}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Renewal process}
-\subsection{Superthreshold firing}
-\subsection{Subthreshold firing}
-\section{Non-renewal processes}
-\subsection{Bursting}
-\subsection{Resonator}
-
-
-\subsection{Standard distributions}
-\subsubsection{Gamma}
-\subsubsection{How to read ISI histograms}
-refractoriness, poisson tail, sub-, supra-threshold, missed spikes
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Correlation with stimulus}
-\subsection{Tuning curve}
-\subsection{Linear filter}
-\subsection{Spatiotemporal receptive field}
-\subsection{Generalized linear model}
 
-\begin{frame}
-\end{frame}
diff --git a/pointprocesses/lecture/pointprocessscetchA.eps b/pointprocesses/lecture/pointprocessscetchA.eps
index 95a12ae..e799c6c 100644
--- a/pointprocesses/lecture/pointprocessscetchA.eps
+++ b/pointprocesses/lecture/pointprocessscetchA.eps
@@ -1,7 +1,7 @@
 %!PS-Adobe-2.0 EPSF-2.0
 %%Title: pointprocessscetchA.tex
 %%Creator: gnuplot 4.6 patchlevel 4
-%%CreationDate: Sun Oct 26 14:09:12 2014
+%%CreationDate: Mon Oct 26 09:31:15 2015
 %%DocumentFonts: 
 %%BoundingBox: 50 50 373 135
 %%EndComments
@@ -430,10 +430,10 @@ SDict begin [
   /Title (pointprocessscetchA.tex)
   /Subject (gnuplot plot)
   /Creator (gnuplot 4.6 patchlevel 4)
-  /Author (jan)
+  /Author (benda)
 %  /Producer (gnuplot)
 %  /Keywords ()
-  /CreationDate (Sun Oct 26 14:09:12 2014)
+  /CreationDate (Mon Oct 26 09:31:15 2015)
   /DOCINFO pdfmark
 end
 } ifelse
diff --git a/pointprocesses/lecture/pointprocessscetchA.pdf b/pointprocesses/lecture/pointprocessscetchA.pdf
index dcc5228..83c76bc 100644
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diff --git a/pointprocesses/lecture/pointprocessscetchB.eps b/pointprocesses/lecture/pointprocessscetchB.eps
index d204109..0e1a2c1 100644
--- a/pointprocesses/lecture/pointprocessscetchB.eps
+++ b/pointprocesses/lecture/pointprocessscetchB.eps
@@ -1,7 +1,7 @@
 %!PS-Adobe-2.0 EPSF-2.0
 %%Title: pointprocessscetchB.tex
 %%Creator: gnuplot 4.6 patchlevel 4
-%%CreationDate: Sun Oct 26 17:34:18 2014
+%%CreationDate: Mon Oct 26 09:31:16 2015
 %%DocumentFonts: 
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 %%EndComments
@@ -430,10 +430,10 @@ SDict begin [
   /Title (pointprocessscetchB.tex)
   /Subject (gnuplot plot)
   /Creator (gnuplot 4.6 patchlevel 4)
-  /Author (jan)
+  /Author (benda)
 %  /Producer (gnuplot)
 %  /Keywords ()
-  /CreationDate (Sun Oct 26 17:34:18 2014)
+  /CreationDate (Mon Oct 26 09:31:16 2015)
   /DOCINFO pdfmark
 end
 } ifelse
diff --git a/pointprocesses/lecture/pointprocessscetchB.pdf b/pointprocesses/lecture/pointprocessscetchB.pdf
index a4c7e8c..09d11f8 100644
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diff --git a/pointprocesses/lecture/rasterexamples.py b/pointprocesses/lecture/rasterexamples.py
new file mode 100644
index 0000000..c7c2433
--- /dev/null
+++ b/pointprocesses/lecture/rasterexamples.py
@@ -0,0 +1,86 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def hompoisson(rate, trials, duration) :
+    spikes = []
+    for k in range(trials) :
+        times = []
+        t = 0.0
+        while t < duration :
+            t += np.random.exponential(1/rate)
+            times.append( t )
+        spikes.append( times )
+    return spikes
+
+def inhompoisson(rate, trials, dt) :
+    spikes = []
+    p = rate*dt
+    for k in range(trials) :
+        x = np.random.rand(len(rate))
+        times = dt*np.nonzero(x<p)[0]
+        spikes.append( times )
+    return spikes
+
+
+def pifspikes(input, trials, dt, D=0.1) :
+    vreset = 0.0
+    vthresh = 1.0
+    tau = 1.0
+    spikes = []
+    for k in range(trials) :
+        times = []
+        v = vreset
+        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
+        for k in xrange(len(noise)) :
+            v += (input[k]+noise[k])*dt/tau
+            if v >= vthresh :
+                v = vreset
+                times.append(k*dt)
+        spikes.append( times )
+    return spikes
+
+# parameter:
+rate = 20.0
+drate = 50.0
+trials = 10
+duration = 2.0
+dt = 0.001
+tau = 0.1;
+
+# homogeneous spike trains:
+homspikes = hompoisson(rate, trials, duration)
+
+# OU noise:
+rng = np.random.RandomState(54637281)
+time = np.arange(0.0, duration, dt)
+x = np.zeros(time.shape)+rate
+n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+for k in xrange(1,len(x)) :
+    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+x[x<0.0] = 0.0
+
+# inhomogeneous spike trains:
+#inhspikes = inhompoisson(x, trials, dt)
+# pif spike trains:
+inhspikes = pifspikes(x, trials, dt, D=0.3)
+
+fig = plt.figure( figsize=(9,4) )
+ax = fig.add_subplot(1, 2, 1)
+ax.set_title('stationary')
+ax.set_xlim(0.0, duration)
+ax.set_ylim(-0.5, trials-0.5)
+ax.set_xlabel('Time [s]')
+ax.set_ylabel('Trials')
+ax.eventplot(homspikes, colors=[[0, 0, 0]], linelength=0.8)
+
+ax = fig.add_subplot(1, 2, 2)
+ax.set_title('non-stationary')
+ax.set_xlim(0.0, duration)
+ax.set_ylim(-0.5, trials-0.5)
+ax.set_xlabel('Time [s]')
+ax.set_ylabel('Trials')
+ax.eventplot(inhspikes, colors=[[0, 0, 0]], linelength=0.8)
+
+plt.tight_layout()
+plt.savefig('rasterexamples.pdf')
+plt.show()
diff --git a/pointprocesses/lecture/returnmapexamples.py b/pointprocesses/lecture/returnmapexamples.py
new file mode 100644
index 0000000..96803ff
--- /dev/null
+++ b/pointprocesses/lecture/returnmapexamples.py
@@ -0,0 +1,105 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def hompoisson(rate, trials, duration) :
+    spikes = []
+    for k in range(trials) :
+        times = []
+        t = 0.0
+        while t < duration :
+            t += np.random.exponential(1/rate)
+            times.append( t )
+        spikes.append( times )
+    return spikes
+
+def inhompoisson(rate, trials, dt) :
+    spikes = []
+    p = rate*dt
+    for k in range(trials) :
+        x = np.random.rand(len(rate))
+        times = dt*np.nonzero(x<p)[0]
+        spikes.append( times )
+    return spikes
+
+
+def pifspikes(input, trials, dt, D=0.1) :
+    vreset = 0.0
+    vthresh = 1.0
+    tau = 1.0
+    spikes = []
+    for k in range(trials) :
+        times = []
+        v = vreset
+        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
+        for k in xrange(len(noise)) :
+            v += (input[k]+noise[k])*dt/tau
+            if v >= vthresh :
+                v = vreset
+                times.append(k*dt)
+        spikes.append( times )
+    return spikes
+
+def isis( spikes ) :
+    isi = []
+    for k in xrange(len(spikes)) :
+        isi.extend(np.diff(spikes[k]))
+    return np.array( isi )
+
+def plotisih( ax, isis, binwidth=None ) :
+    if binwidth == None :
+        nperbin = 200.0    # average number of isis per bin
+        bins = len(isis)/nperbin  # number of bins
+        binwidth = np.max(isis)/bins
+        if binwidth < 5e-4 :     # half a millisecond
+            binwidth = 5e-4
+    h, b = np.histogram(isis, np.arange(0.0, np.max(isis)+binwidth, binwidth), density=True)
+    ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.set_xlabel('ISI [ms]')
+    ax.set_ylabel('p(ISI) [1/s]')
+    ax.bar( 1000.0*b[:-1], h, 1000.0*np.diff(b) )
+
+def plotreturnmap(ax, isis, lag=1, max=None) :
+    ax.set_xlabel(r'ISI$_i$ [ms]')
+    ax.set_ylabel(r'ISI$_{i+1}$ [ms]')
+    if max != None :
+        ax.set_xlim(0.0, 1000.0*max)
+        ax.set_ylim(0.0, 1000.0*max)
+    ax.scatter( 1000.0*isis[:-lag], 1000.0*isis[lag:] )
+
+# parameter:
+rate = 20.0
+drate = 50.0
+trials = 10
+duration = 10.0
+dt = 0.001
+tau = 0.1;
+
+# homogeneous spike trains:
+homspikes = hompoisson(rate, trials, duration)
+
+# OU noise:
+rng = np.random.RandomState(54637281)
+time = np.arange(0.0, duration, dt)
+x = np.zeros(time.shape)+rate
+n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+for k in xrange(1,len(x)) :
+    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+x[x<0.0] = 0.0
+
+# pif spike trains:
+inhspikes = pifspikes(x, trials, dt, D=0.3)
+
+fig = plt.figure( figsize=(9,4) )
+ax = fig.add_subplot(1, 2, 1)
+ax.set_title('stationary')
+plotreturnmap(ax, isis(homspikes), 1, 0.3)
+
+ax = fig.add_subplot(1, 2, 2)
+ax.set_title('non-stationary')
+plotreturnmap(ax, isis(inhspikes), 1, 0.3)
+
+plt.tight_layout()
+plt.savefig('returnmapexamples.pdf')
+#plt.show()
diff --git a/pointprocesses/lecture/serialcorrexamples.py b/pointprocesses/lecture/serialcorrexamples.py
new file mode 100644
index 0000000..e7fff9c
--- /dev/null
+++ b/pointprocesses/lecture/serialcorrexamples.py
@@ -0,0 +1,117 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def hompoisson(rate, trials, duration) :
+    spikes = []
+    for k in range(trials) :
+        times = []
+        t = 0.0
+        while t < duration :
+            t += np.random.exponential(1/rate)
+            times.append( t )
+        spikes.append( times )
+    return spikes
+
+def inhompoisson(rate, trials, dt) :
+    spikes = []
+    p = rate*dt
+    for k in range(trials) :
+        x = np.random.rand(len(rate))
+        times = dt*np.nonzero(x<p)[0]
+        spikes.append( times )
+    return spikes
+
+
+def pifspikes(input, trials, dt, D=0.1) :
+    vreset = 0.0
+    vthresh = 1.0
+    tau = 1.0
+    spikes = []
+    for k in range(trials) :
+        times = []
+        v = vreset
+        noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
+        for k in xrange(len(noise)) :
+            v += (input[k]+noise[k])*dt/tau
+            if v >= vthresh :
+                v = vreset
+                times.append(k*dt)
+        spikes.append( times )
+    return spikes
+
+def isis( spikes ) :
+    isi = []
+    for k in xrange(len(spikes)) :
+        isi.extend(np.diff(spikes[k]))
+    return np.array( isi )
+
+def plotisih( ax, isis, binwidth=None ) :
+    if binwidth == None :
+        nperbin = 200.0    # average number of isis per bin
+        bins = len(isis)/nperbin  # number of bins
+        binwidth = np.max(isis)/bins
+        if binwidth < 5e-4 :     # half a millisecond
+            binwidth = 5e-4
+    h, b = np.histogram(isis, np.arange(0.0, np.max(isis)+binwidth, binwidth), density=True)
+    ax.text(0.9, 0.85, 'rate={:.0f}Hz'.format(1.0/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.75, 'mean={:.0f}ms'.format(1000.0*np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.text(0.9, 0.65, 'CV={:.2f}'.format(np.std(isis)/np.mean(isis)), ha='right', transform=ax.transAxes)
+    ax.set_xlabel('ISI [ms]')
+    ax.set_ylabel('p(ISI) [1/s]')
+    ax.bar( 1000.0*b[:-1], h, 1000.0*np.diff(b) )
+
+def plotreturnmap(ax, isis, lag=1, max=None) :
+    ax.set_xlabel(r'ISI$_i$ [ms]')
+    ax.set_ylabel(r'ISI$_{i+1}$ [ms]')
+    if max != None :
+        ax.set_xlim(0.0, 1000.0*max)
+        ax.set_ylim(0.0, 1000.0*max)
+    ax.scatter( 1000.0*isis[:-lag], 1000.0*isis[lag:] )
+
+def plotserialcorr(ax, isis, maxlag=10) :
+    lags = np.arange(maxlag+1)
+    corr = [1.0]
+    for lag in lags[1:] :
+        corr.append(np.corrcoef(isis[:-lag], isis[lag:])[0,1])
+    ax.set_xlabel(r'lag $k$')
+    ax.set_ylabel(r'ISI correlation $\rho_k$')
+    ax.set_xlim(0.0, maxlag)
+    ax.set_ylim(-1.0, 1.0)
+    ax.plot(lags, corr, '.-', markersize=20)
+
+# parameter:
+rate = 20.0
+drate = 50.0
+trials = 10
+duration = 500.0
+dt = 0.001
+tau = 0.1;
+
+# homogeneous spike trains:
+homspikes = hompoisson(rate, trials, duration)
+
+# OU noise:
+rng = np.random.RandomState(54637281)
+time = np.arange(0.0, duration, dt)
+x = np.zeros(time.shape)+rate
+n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
+for k in xrange(1,len(x)) :
+    x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
+x[x<0.0] = 0.0
+
+# pif spike trains:
+inhspikes = pifspikes(x, trials, dt, D=0.3)
+
+fig = plt.figure( figsize=(9,3) )
+
+ax = fig.add_subplot(1, 2, 1)
+plotserialcorr(ax, isis(homspikes))
+ax.set_ylim(-0.2, 1.0)
+
+ax = fig.add_subplot(1, 2, 2)
+plotserialcorr(ax, isis(inhspikes))
+ax.set_ylim(-0.2, 1.0)
+
+plt.tight_layout()
+plt.savefig('serialcorrexamples.pdf')
+#plt.show()
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diff --git a/programming/lectures/images/line_graph1_3.png b/programming/lectures/images/line_graph1_3.png
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diff --git a/programming/lectures/images/misleading_pie.png b/programming/lectures/images/misleading_pie.png
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index 0000000..3dae0cd
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diff --git a/programming/lectures/images/nobelbad.png b/programming/lectures/images/nobelbad.png
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index 0000000..4602b7c
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diff --git a/programming/lectures/images/one_d_problem_c.pdf b/programming/lectures/images/one_d_problem_c.pdf
new file mode 100644
index 0000000..31974db
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diff --git a/programming/lectures/images/properly_scaled_graph.png b/programming/lectures/images/properly_scaled_graph.png
new file mode 100644
index 0000000..1a5de32
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diff --git a/programming/lectures/images/sample_pie.png b/programming/lectures/images/sample_pie.png
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index 0000000..937a231
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diff --git a/programming/lectures/plotting_spike_trains.tex b/programming/lectures/plotting_spike_trains.tex
index cb85c51..13d9e06 100644
--- a/programming/lectures/plotting_spike_trains.tex
+++ b/programming/lectures/plotting_spike_trains.tex
@@ -86,6 +86,8 @@
 \end{flushright}
 }
 
+\newcommand{\code}[1]{\texttt{#1}}
+
 \input{../../latex/environments.tex}
 \makeatother
  
@@ -103,11 +105,259 @@
   \begin{enumerate}
   \item Graphische Darstellung von Daten
   \item Spiketrain Analyse
-  \item \"Ubungen, \"Ubungen, \"Ubungen. 
   \end{enumerate}
 \end{frame}
 
 
+\begin{frame}[plain]
+  \huge{1. Graphische Darstellung von Daten}\pause
+  \begin{figure}
+    \includegraphics[width=0.9\columnwidth]{images/convincing}
+  \end{figure}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Graphische Darstellung von Daten}
+  \framesubtitle{Was soll ein Datenplot erreichen?}
+
+  \begin{itemize}
+  \item Ist eine m\"oglichst neutrale Darstellung der Daten.
+  \item Soll dem Leser die Daten greifbar machen und die Aussagen der
+    Analyse darstellen.
+  \item Erlaubt dem Leser die gezeigten Effekte selbst zu beguachten
+    und zu validieren.
+  \item Muss vollst\"andig annotiert sein.
+  \item Folgt dem Prinzip der \textbf{ink minimization}. (Das
+    Verh\"altnis aus Tinte, die f\"ur die Darstellung der Daten
+    gebraucht wird und der Menge Tinte, die f\"ur die Graphik
+    ben\"otigt wird sollte m\"oglichst gro{\ss} sein )
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Graphische Darstellung von Daten}
+  \framesubtitle{Was sollte vermieden werden?}
+  
+  \begin{itemize}
+  \item Suggestive oder gar fehlleitende Darstellung.
+  \item Ablenkung durch unruhige oder \"uberm\"a{\ss}ige Effekte.
+  \item Comicartige Effekte...
+  \end{itemize}\pause
+  \begin{figure}
+    \includegraphics[width=0.35\columnwidth]{images/one_d_problem_c}
+  \end{figure}\pause
+  ... aus{\ss}er sie werden rein zur Illustration benutzt ohne einen
+  Anspruch auf Richtigkeit zu erheben.
+\end{frame}
+
+\begin{frame}
+  \frametitle{Graphische Darstellung von Daten}
+  \framesubtitle{Suboptimale Beispiele}
+  \only <1> {
+    \begin{figure}
+      \includegraphics[width=0.5\columnwidth]{images/nobelbad}
+    \end{figure}
+    \vspace{0.25cm}
+    Aus Hafting et al., Nature, 2005
+  }
+  \only <2> {
+    \begin{figure}
+      \includegraphics[width=0.7\columnwidth]{images/misleading_pie}
+    \end{figure}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+  }
+  \only <3> {
+    \begin{figure}
+      \includegraphics[width=0.7\columnwidth]{images/sample_pie}
+    \end{figure}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+  }
+  \only <4> {
+    \begin{figure}
+      \includegraphics[width=0.4\columnwidth]{images/badbarright}
+    \end{figure}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+  }
+  \only <5> {
+    \begin{figure}
+      \includegraphics[width=0.4\columnwidth]{images/badbarleft}
+    \end{figure}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+  }
+  \only <6> {
+    \begin{figure}
+      \includegraphics[width=0.8\columnwidth]{images/badbarplot}
+    \end{figure}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+  }
+  \only <7> {
+    Wahl der Zeichenfl\"ache kann den visuellen Eindruck beeinflu{\ss}en.
+    \begin{columns}
+      \begin{column}{4.cm}
+        \begin{figure}
+          \includegraphics[width=0.7\columnwidth]{images/line_graph1}
+        \end{figure}
+      \end{column}
+
+      \begin{column}{4.cm}
+        \begin{figure}
+          \includegraphics[width=0.7\columnwidth]{images/line_graph1_3}
+        \end{figure}
+      \end{column}
+
+      \begin{column}{4.cm}
+        \begin{figure}
+          \includegraphics[width=0.7\columnwidth]{images/line_graph1_4}
+        \end{figure}
+      \end{column}
+    \end{columns}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+  }
+    \only <8> {
+     Vorsicht bei der Skalierung von Symbolen!
+    \begin{columns}
+      \begin{column}{4.cm}
+        \begin{figure}
+          \includegraphics[width=0.7\columnwidth]{images/improperly_scaled_graph}
+        \end{figure}
+      \end{column}
+
+      \begin{column}{4.cm}
+        \begin{figure}
+          \includegraphics[width=0.7\columnwidth]{images/properly_scaled_graph}
+        \end{figure}
+      \end{column}
+
+      \begin{column}{4.cm}
+        \begin{figure}
+          \includegraphics[width=0.7\columnwidth]{images/comparison_properly_improperly_graph}
+        \end{figure}
+      \end{column}
+    \end{columns}
+    \vspace{0.5cm}
+    \url{https://en.wikipedia.org/wiki/Misleading_graph}
+   }
+\end{frame}
+
+\begin{frame}[fragile]
+  \frametitle{Graphische Darstellung von Daten}
+  \framesubtitle{Plotting Interfaces in Matlab}
+  Es gibt zwei Wege Graphen zu bearbeiten:
+  \begin{enumerate}
+  \item Interaktiv \"uber das \textit{graphische User Interface}\pause
+  \item Die Kommandozeile bzw. in Skripten und Funktionen.\pause
+  \end{enumerate}
+  Beides hat seine Berechtigung und seine eigenen Vor- und Nachteile. Welche?
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Graphische Darstellung von Daten}
+  \framesubtitle{Ver\"anderung des Graphen \"uber die Kommandozeile}
+  \begin{itemize}
+  \item Erstellt ein Skript, dass einen Plot erstellt.
+  \item Dieser soll zwei Sinus unterschiedlicher Frequenz darstellen.
+  \end{itemize}
+  Wir werden jetzt die Kommandozeil bzw. das Skript verbessern um den
+  Plot ``sch\"oner'' zu machen.
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Graphische Darstellung von Daten}
+  \framesubtitle{Ver\"anderung des Graphen \"uber die Kommandozeile}
+  \begin{enumerate}
+  \item Einstellungen der Linienplots:
+    \begin{itemize}
+    \item St\"arke und Farbe.
+    \item Linienstil, Marker.
+    \end{itemize}\pause
+  \item Achsbeschriftung:
+    \begin{itemize}
+    \item \code{xlabel}, \code{ylabel}.
+    \item Schriftart und Gr\"o{\ss}e.
+    \end{itemize}\pause
+  \item Achsenskalierung und Ticks:
+    \begin{itemize}
+    \item Skalierung der Achsen (Minumum und Maxmimum, logarithmisch oder linear).
+    \item Manuelles Setzen der Ticks, ihrer Richtung und Beschriftung.
+    \item Grid or no Grid?
+    \end{itemize}\pause
+  \item Setzen von globalen Parametern:
+    \begin{itemize}
+    \item Einstellung der Papiergr\"o{\ss}e und plzieren der
+      Zeichenfl\"ache.
+    \item Box oder nicht?
+    \item Speichern der Abbildung als pdf.      
+    \end{itemize}
+  \end{enumerate}
+\end{frame}
+
+
+\begin{frame} [fragile]
+ \frametitle{Graphische Darstellung von Daten}
+ \framesubtitle{Ver\"andern von Eigenschaften \"uber die Kommandozeile}
+ \vspace{-0.75em}
+ \scriptsize
+  \begin{lstlisting}
+fig = figure();
+set(gcf, 'PaperUnits', 'centimeters', 'PaperSize', [11.7 9.0]);
+set(gcf, 'PaperPosition',[0.0 0.0 11.7 9.0], 'Color', 'white')
+hold on
+plot(time, neuronal_data, 'color', [ 0.2 0.5 0.7], 'linewidth', 1.)
+plot(spike_times, ones(size(spike_times))*threshold, 'ro', 'markersize', 4)
+line([time(1) time(end)], [threshold threshold], 'linestyle', '--',
+    'linewidth', 0.75, 'color', [0.9 0.9 0.9])
+ylim([0 35])
+xlim([0 2.25])
+box('off')
+xlabel('time [s]', 'fontname', 'MyriadPro-Regular', 'fontsize', 10)
+ylabel('potential [mV]', 'fontname', 'MyriadPro-Regular', 'fontsize', 10)
+title('pyramidal cell', 'fontname', 'MyriadPro-Regular', 'fontsize', 12)
+set(gca, 'TickDir','out', 'linewidth', 1.5, 'fontname', 'MyriadPro-Regular')
+saveas(fig, 'spike_detection.pdf', 'pdf')
+\end{lstlisting}
+\end{frame}
+
+
+\begin{frame} [fragile]
+ \frametitle{Graphische Darstellung von Daten}
+ \framesubtitle{Ver\"andern von Eigenschaften \"uber die Kommandozeile}
+     \begin{figure}
+      \centering
+      \includegraphics[width=0.75\columnwidth]{./images/spike_detection}
+    \end{figure}
+\end{frame}
+
+
+\begin{frame} [fragile]
+ \frametitle{Graphische Darstellung von Daten}
+ \framesubtitle{Welche Art Plot wof\"ur?}
+ \url{http://www.mathworks.de/discovery/gallery.html}
+\end{frame}
+
+
+\begin{frame} [fragile]
+ \frametitle{Graphische Darstellung von Daten}
+ \framesubtitle{Was macht einen guten Abbildung aus?}
+ \begin{enumerate}
+ \item Klarheit.
+ \item Vollstaendige Beschriftung.
+ \item Deutliche Unterscheidbarkeit von Kurven.
+ \item Keine suggestive Darstellung. 
+ \item Ausgewogenheit von Linienst\"arken Schrift- und Plotgr\"o{\ss}e.
+ \item Vermeidung von Suggestiven Darstellungen.
+ \item Fehlerbalken, wenn sie angebracht sind.
+ \end{enumerate}
+\end{frame}
+
+
 \begin{frame}[plain]
 \huge{2. Spiketrain Analyse I}
 \end{frame}
diff --git a/scientificcomputing-script.tex b/scientificcomputing-script.tex
index 306a84a..a053861 100644
--- a/scientificcomputing-script.tex
+++ b/scientificcomputing-script.tex
@@ -59,7 +59,8 @@
 
 % figures:
 \setlength{\fboxsep}{0pt}
-\newcommand{\texpicture}[1]{{\sffamily\footnotesize\input{#1.tex}}}
+\newcommand{\texinputpath}{}
+\newcommand{\texpicture}[1]{{\sffamily\footnotesize\input{\texinputpath#1.tex}}}
 %\newcommand{\texpicture}[1]{\fbox{\sffamily\footnotesize\input{#1.tex}}}
 %\newcommand{\texpicture}[1]{\setlength{\fboxsep}{2mm}\fbox{#1}}
 %\newcommand{\texpicture}[1]{}
@@ -204,8 +205,9 @@
 \newenvironment{definition}[1][]{\medskip\noindent\textbf{Definition}\ifthenelse{\equal{#1}{}}{}{ #1}:\newline}%
   {\medskip}
 
+%%%%% exercises: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \newcounter{maxexercise} 
-\setcounter{maxexercise}{9}  % show listings up to exercise maxexercise
+\setcounter{maxexercise}{10}  % show listings up to exercise maxexercise
 \newcounter{theexercise} 
 \setcounter{theexercise}{1}
 \newcommand{\codepath}{}
@@ -213,7 +215,7 @@
   \arabic{theexercise}:}\newline \newcommand{\exercisesource}{#1}}%
   {\ifthenelse{\equal{\exercisesource}{}}{}{\ifthenelse{\value{theexercise}>\value{maxexercise}}{}{\medskip\lstinputlisting{\codepath\exercisesource}}}\medskip\stepcounter{theexercise}}
 
-\graphicspath{{statistics/lecture/}{statistics/lecture/figures/}{bootstrap/lecture/}{bootstrap/lecture/figures/}{likelihood/lecture/}{likelihood/lecture/figures/}}
+\graphicspath{{statistics/lecture/}{statistics/lecture/figures/}{bootstrap/lecture/}{bootstrap/lecture/figures/}{likelihood/lecture/}{likelihood/lecture/figures/}{pointprocesses/lecture/}{pointprocesses/lecture/figures/}}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -233,4 +235,8 @@
 \renewcommand{\codepath}{likelihood/code/}
 \include{likelihood/lecture/likelihood}
 
+\renewcommand{\codepath}{pointprocesses/code/}
+\renewcommand{\texinputpath}{pointprocesses/lecture/}
+%\include{pointprocesses/lecture/pointprocesses}
+
 \end{document}
diff --git a/statistics/code/boltzmann.m b/statistics/code/boltzmann.m
new file mode 100644
index 0000000..8190460
--- /dev/null
+++ b/statistics/code/boltzmann.m
@@ -0,0 +1,7 @@
+function y = boltzmann(parameter, x)
+% parameter 1: alpha
+% parameter 2: k
+% parameter 3: x_0
+% parameter 4: y_0
+
+y = (parameter(1) ./ (1 + exp(-parameter(2) .* (x - parameter(3))))) + parameter(4);
\ No newline at end of file
diff --git a/statistics/code/create_linear_data.m b/statistics/code/create_linear_data.m
new file mode 100644
index 0000000..f929f23
--- /dev/null
+++ b/statistics/code/create_linear_data.m
@@ -0,0 +1,7 @@
+function y = create_linear_data(x)
+
+m = 2.5;
+n = -0.35;
+d = 2.5;
+
+y = x .* m + n + randn(size(x)) .* d;
diff --git a/statistics/code/estimate_regression.m b/statistics/code/estimate_regression.m
new file mode 100644
index 0000000..fd3d2c4
--- /dev/null
+++ b/statistics/code/estimate_regression.m
@@ -0,0 +1,7 @@
+function param = estimate_regression(x,y,p_0)
+
+objective_function = @(p)lsq_error(p, x, y);
+param = fminunc(objective_function, p_0);
+disp(param)
+param1 = fminsearch(objective_function, p_0);
+disp(param1)
\ No newline at end of file
diff --git a/statistics/code/exponential.m b/statistics/code/exponential.m
new file mode 100644
index 0000000..1bb2a04
--- /dev/null
+++ b/statistics/code/exponential.m
@@ -0,0 +1,5 @@
+function y = exponential(parameter, x)
+% Function implements an exponential function with two parameters
+% controlling the amplitude and the time constant.
+
+y = parameter(1) .* exp(x./parameter(2));
\ No newline at end of file
diff --git a/statistics/code/lsq_gradient_sigmoid.m b/statistics/code/lsq_gradient_sigmoid.m
new file mode 100644
index 0000000..05e9721
--- /dev/null
+++ b/statistics/code/lsq_gradient_sigmoid.m
@@ -0,0 +1,9 @@
+function gradient = lsq_gradient_sigmoid(parameter, x, y)
+h = 1e-6;
+
+gradient = zeros(size(parameter));
+for i = 1:length(parameter)
+    parameter_h = parameter;
+    parameter_h(i) = parameter_h(i) + h;
+    gradient(i) = (lsq_sigmoid_error(parameter_h, x, y) - lsq_sigmoid_error(parameter, x, y)) / h;
+end
\ No newline at end of file
diff --git a/statistics/code/lsq_sigmoid_error.m b/statistics/code/lsq_sigmoid_error.m
new file mode 100644
index 0000000..5f05f21
--- /dev/null
+++ b/statistics/code/lsq_sigmoid_error.m
@@ -0,0 +1,8 @@
+function error = lsq_sigmoid_error(parameter, x, y)
+% p(1) the amplitude
+% p(2) the slope
+% p(3) the x-shift
+% p(4) the y-shift
+
+y_est = parameter(1)./(1+ exp(-parameter(2) .* (x - parameter(3)))) + parameter(4);
+error = mean((y_est - y).^2);
\ No newline at end of file
diff --git a/statistics/code/sigmoidal_gradient_descent.m b/statistics/code/sigmoidal_gradient_descent.m
new file mode 100644
index 0000000..9763f5a
--- /dev/null
+++ b/statistics/code/sigmoidal_gradient_descent.m
@@ -0,0 +1,44 @@
+
+
+%% fit the sigmoid
+
+clear 
+close all
+
+load('iv_curve.mat')
+
+figure()
+plot(voltage, current, 'o')
+xlabel('voltate [mV]')
+ylabel('current [pA]')
+
+% amplitude, slope, x-shift, y-shift
+%parameter = [10 0.25 -50, 2.5];
+parameter = [20 0.5 -50, 2.5];
+
+eps = 0.1;
+% do the descent
+gradient = [];
+steps = 0;
+error = [];
+
+while isempty(gradient) || norm(gradient) > 0.01
+    steps = steps + 1; 
+    gradient = lsq_gradient_sigmoid(parameter, voltage, current);
+    error(steps) = lsq_sigmoid_error(parameter, voltage, current);
+    parameter = parameter - eps .* gradient; 
+end
+plot(1:steps, error)
+
+disp('gradient descent done!')
+disp(strcat('final position: ', num2str(parameter)))
+disp(strcat('final error: ', num2str(error(end))))
+
+%% use fminsearch
+parameter = [10 0.5 -50, 2.5];
+
+objective_function = @(p)lsq_sigmoid_error(p, voltage, current);
+param = fminunc(objective_function, parameter);
+disp(param)
+param1 = fminsearch(objective_function, parameter);
+disp(param1)
diff --git a/statistics/exercises/mlestd.m b/statistics/exercises/mlestd.m
index 61f4b22..de37a56 100644
--- a/statistics/exercises/mlestd.m
+++ b/statistics/exercises/mlestd.m
@@ -27,4 +27,4 @@ subplot(1, 2, 2);
 plot(psigs, loglm);
 xlabel('standard deviation')
 ylabel('log likelihood')
-savefigpdf(gcf, 'mlestd.pdf', 12, 5);
+savefigpdf(gcf, 'mlestd.pdf', 15, 5);
diff --git a/statistics/exercises/mlestd.pdf b/statistics/exercises/mlestd.pdf
index dad420c..f01f927 100644
Binary files a/statistics/exercises/mlestd.pdf and b/statistics/exercises/mlestd.pdf differ
diff --git a/statistics/exercises/statistics04.tex b/statistics/exercises/statistics04.tex
index 276f7ea..1c3dcdf 100644
--- a/statistics/exercises/statistics04.tex
+++ b/statistics/exercises/statistics04.tex
@@ -113,8 +113,10 @@ Absch\"atzung der Standardabweichung verdeutlichen.
 \continue
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \question \qt{Maximum-Likelihood-Sch\"atzer einer Ursprungsgeraden} 
-In der Vorlesung haben wir eine Gleichung f\"ur die Maximum-Likelihood
-Absch\"atzung der Steigung einer Ursprungsgeraden hergeleitet.
+In der Vorlesung haben wir folgende Formel f\"ur die Maximum-Likelihood
+Absch\"atzung der Steigung $\theta$ einer Ursprungsgeraden durch $n$ Datenpunkte $(x_i|y_i)$ mit Standardabweichung $\sigma_i$ hergeleitet:
+\[\theta = \frac{\sum_{i=1}^n \frac{x_iy_i}{\sigma_i^2}}{ \sum_{i=1}^n
+  \frac{x_i^2}{\sigma_i^2}} \]
 \begin{parts}
   \part \label{mleslopefunc} Schreibe eine Funktion, die in einem $x$ und einem
   $y$ Vektor die Datenpaare \"uberreicht bekommt und die Steigung der
@@ -146,13 +148,12 @@ nicht so einfach wie der Mittelwert und die Standardabweichung einer
 Normalverteilung direkt aus den Daten berechnet werden k\"onnen. Solche Parameter
 m\"ussen dann aus den Daten mit der Maximum-Likelihood-Methode gefittet werden.
 
-Um dies zu veranschaulichen ziehen wir uns diesmal Zufallszahlen, die nicht einer
-Normalverteilung entstammen, sonder aus der Gamma-Verteilung.
+Um dies zu veranschaulichen ziehen wir uns diesmal nicht normalverteilte Zufallszahlen, sondern Zufallszahlen aus der Gamma-Verteilung.
 \begin{parts}
   \part
-  Finde heraus welche Funktion die Wahrscheinlichkeitsdichtefunktion
-  (probability density function) der Gamma-Verteilung in \code{matlab}
-  berechnet.
+  Finde heraus welche \code{matlab} Funktion die
+  Wahrscheinlichkeitsdichtefunktion (probability density function) der
+  Gamma-Verteilung berechnet.
 
   \part
   Plotte mit Hilfe dieser Funktion die  Wahrscheinlichkeitsdichtefunktion
@@ -169,17 +170,17 @@ Normalverteilung entstammen, sonder aus der Gamma-Verteilung.
 
   \part
   Finde heraus mit welcher \code{matlab}-Funktion eine beliebige
-  Verteilung (``distribution'') und die Gammaverteilung an die
-  Zufallszahlen nach der Maximum-Likelihood Methode gefittet werden
-  kann.
+  Verteilung (``distribution'') an die Zufallszahlen nach der
+  Maximum-Likelihood Methode gefittet werden kann. Wie wird diese
+  Funktion benutzt, um die Gammaverteilung an die Daten zu fitten?
 
   \part
-  Bestimme mit dieser Funktion die Parameter der
-  Gammaverteilung aus den Zufallszahlen. 
+  Bestimme mit dieser Funktion die Parameter der Gammaverteilung aus
+  den Zufallszahlen.
 
   \part
-  Plotte anschlie{\ss}end
-  die Gammaverteilung mit den gefitteten Parametern.
+  Plotte anschlie{\ss}end die Gammaverteilung mit den gefitteten
+  Parametern.
 \end{parts}
 \begin{solution}
   \lstinputlisting{mlepdffit.m}
diff --git a/statistics/lecture/Makefile b/statistics/lecture/Makefile
index 445913e..ec84405 100644
--- a/statistics/lecture/Makefile
+++ b/statistics/lecture/Makefile
@@ -1,22 +1,29 @@
 BASENAME=statistics
+
 PYFILES=$(wildcard *.py)
 PYPDFFILES=$(PYFILES:.py=.pdf)
 
-pdf : $(BASENAME)-chapter.pdf $(PYPDFFILES)
+all : pdf 
+
+# script:
+pdf : $(BASENAME)-chapter.pdf
 
-$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex
+$(BASENAME)-chapter.pdf : $(BASENAME)-chapter.tex $(BASENAME).tex $(PYPDFFILES)
 	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
 
 $(PYPDFFILES) : %.pdf : %.py
 	python $<
 
 clean :
-	rm -f *~ $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out $(BASENAME).aux $(BASENAME).log
+	rm -f *~ 
+	rm -f $(BASENAME).aux $(BASENAME).log
+	rm -f $(BASENAME)-chapter.aux $(BASENAME)-chapter.log $(BASENAME)-chapter.out
+	rm -f $(PYPDFFILES) $(GPTTEXFILES)
 
 cleanall : clean
 	rm -f $(BASENAME)-chapter.pdf
 
-watch :
+watchpdf :
 	while true; do ! make -q pdf && make pdf; sleep 0.5; done
 
 
diff --git a/statistics/lecture/boxwhisker.py b/statistics/lecture/boxwhisker.py
index 1209f7e..38a0dcb 100644
--- a/statistics/lecture/boxwhisker.py
+++ b/statistics/lecture/boxwhisker.py
@@ -43,5 +43,5 @@ ax.annotate('maximum',
 ax.boxplot( x, whis=100.0 )
 plt.tight_layout()
 plt.savefig('boxwhisker.pdf')
-plt.show()
+#plt.show()
 
diff --git a/statistics/lecture/correlation.py b/statistics/lecture/correlation.py
index db317c7..2fa85d4 100644
--- a/statistics/lecture/correlation.py
+++ b/statistics/lecture/correlation.py
@@ -5,7 +5,6 @@ plt.xkcd()
 fig = plt.figure( figsize=(6,5) )
 n = 200
 for k, r  in enumerate( [ 1.0, 0.6, 0.0, -0.9 ] ) :
-    print r
     x = np.random.randn( n )
     y = r*x + np.sqrt(1.0-r*r)*np.random.randn( n )
     ax = fig.add_subplot( 2, 2, k+1 )
@@ -30,5 +29,4 @@ for k, r  in enumerate( [ 1.0, 0.6, 0.0, -0.9 ] ) :
 
 plt.tight_layout()
 plt.savefig('correlation.pdf')
-plt.show()
-
+#plt.show()
diff --git a/statistics/lecture/diehistograms.py b/statistics/lecture/diehistograms.py
index d5e0380..a8832e0 100644
--- a/statistics/lecture/diehistograms.py
+++ b/statistics/lecture/diehistograms.py
@@ -28,5 +28,4 @@ ax.set_ylabel( 'Probability' )
 ax.hist([x2, x1], bins, normed=True, color=['#FFCC00', '#FFFF66' ])
 plt.tight_layout()
 fig.savefig( 'diehistograms.pdf' )
-plt.show()
-
+#plt.show()
diff --git a/statistics/lecture/median.py b/statistics/lecture/median.py
index 2bf420c..a1231f9 100644
--- a/statistics/lecture/median.py
+++ b/statistics/lecture/median.py
@@ -29,5 +29,4 @@ ax.plot(x,g, 'b', lw=4)
 ax.plot([0.0, 0.0], [0.0, 0.45], 'k', lw=2 )
 plt.tight_layout()
 fig.savefig( 'median.pdf' )
-plt.show()
-
+#plt.show()
diff --git a/statistics/lecture/nonlincorrelation.py b/statistics/lecture/nonlincorrelation.py
index c0ca723..498d985 100644
--- a/statistics/lecture/nonlincorrelation.py
+++ b/statistics/lecture/nonlincorrelation.py
@@ -39,4 +39,4 @@ ax.scatter( x, z )
 
 plt.tight_layout()
 plt.savefig('nonlincorrelation.pdf')
-plt.show()
+#plt.show()
diff --git a/statistics/lecture/pdfhistogram.py b/statistics/lecture/pdfhistogram.py
index 039b524..a61460e 100644
--- a/statistics/lecture/pdfhistogram.py
+++ b/statistics/lecture/pdfhistogram.py
@@ -35,5 +35,5 @@ ax.hist(r, 20, normed=True, color='#FFCC00')
 
 plt.tight_layout()
 fig.savefig( 'pdfhistogram.pdf' )
-plt.show()
+#plt.show()
 
diff --git a/statistics/lecture/pdfprobabilities.py b/statistics/lecture/pdfprobabilities.py
index 6481da1..bcbaa09 100644
--- a/statistics/lecture/pdfprobabilities.py
+++ b/statistics/lecture/pdfprobabilities.py
@@ -32,5 +32,4 @@ ax.fill_between( x[(x>x1)&(x<x2)], 0.0, g[(x>x1)&(x<x2)], color='#cc0000' )
 ax.plot(x,g, 'b', lw=4)
 plt.tight_layout()
 fig.savefig( 'pdfprobabilities.pdf' )
-plt.show()
-
+#plt.show()
diff --git a/statistics/lecture/quartile.py b/statistics/lecture/quartile.py
index 34b9960..dbdd202 100644
--- a/statistics/lecture/quartile.py
+++ b/statistics/lecture/quartile.py
@@ -46,5 +46,4 @@ ax.plot([q[0], q[0]], [0.0, 0.4], 'k', lw=2 )
 ax.plot([q[2], q[2]], [0.0, 0.4], 'k', lw=2 )
 plt.tight_layout()
 fig.savefig( 'quartile.pdf' )
-plt.show()
-
+#plt.show()
diff --git a/statistics/lecture/statistics-slides.tex b/statistics/lecture/statistics-slides.tex
new file mode 100644
index 0000000..dc1af03
--- /dev/null
+++ b/statistics/lecture/statistics-slides.tex
@@ -0,0 +1,133 @@
+\documentclass{beamer}
+
+%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\title[]{Scientific Computing --- Statistics}
+\author[]{Jan Benda}
+\institute[]{Neuroethology}
+\date[]{WS 14/15}
+\titlegraphic{\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
+
+%%%%% beamer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\mode<presentation>
+{
+  \usetheme{Singapore}
+  \setbeamercovered{opaque}
+  \usecolortheme{tuebingen}
+  \setbeamertemplate{navigation symbols}{}
+  \usefonttheme{default}
+  \useoutertheme{infolines}
+  % \useoutertheme{miniframes}
+}
+
+%\AtBeginSection[]
+%{
+%  \begin{frame}<beamer>
+%    \begin{center}
+%      \Huge \insertsectionhead
+%    \end{center}
+%  \end{frame}
+%}
+
+\setbeamertemplate{blocks}[rounded][shadow=true]
+\setcounter{tocdepth}{1}
+
+%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[english]{babel}
+\usepackage{amsmath}
+\usepackage{bm} 
+\usepackage{pslatex}   % nice font for pdf file
+%\usepackage{multimedia}
+
+\usepackage{dsfont}
+\newcommand{\naZ}{\mathds{N}}
+\newcommand{\gaZ}{\mathds{Z}}
+\newcommand{\raZ}{\mathds{Q}}
+\newcommand{\reZ}{\mathds{R}}
+\newcommand{\reZp}{\mathds{R^+}}
+\newcommand{\reZpN}{\mathds{R^+_0}}
+\newcommand{\koZ}{\mathds{C}}
+
+%%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{graphicx}
+\newcommand{\texpicture}[1]{{\sffamily\small\input{#1.tex}}}
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+ basicstyle=\ttfamily,
+ numbers=left,
+ showstringspaces=false,
+ language=Matlab,
+ commentstyle=\itshape\color{darkgray},
+ keywordstyle=\color{blue},
+ stringstyle=\color{green},
+ backgroundcolor=\color{blue!10},
+ breaklines=true,
+ breakautoindent=true,
+ columns=flexible,
+ frame=single,
+ captionpos=b,
+ xleftmargin=1em,
+ xrightmargin=1em,
+ aboveskip=10pt
+ }
+
+\graphicspath{{figures/}}
+
+ 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document} 
+
+\begin{frame}[plain]
+  \frametitle{}
+  \vspace{-1cm}
+  \titlepage % erzeugt Titelseite
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Content}
+  \tableofcontents
+\end{frame}
+
+
+\subsection{What is inferential statistics?}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{sources of error in an experiment}
+  \begin{task}{Think about it for 2 min}
+    If you repeat a scientific experiment, why do you not get the same
+    result every time you repeat it?
+  \end{task}
+  \pause
+  \begin{itemize}
+  \item sampling error (a finite subset of the population of interest
+    is selected in each experiment)
+  \item nonsampling errors (e.g. noise, uncontrolled factors)
+  \end{itemize}
+\end{frame}
+
+% ----------------------------------------------------------
+\begin{frame}[fragile]
+\frametitle{statisticians are lazy}
+\Large
+\only<1>{
+  \begin{center}
+    \includegraphics[width=.8\linewidth]{2012-10-29_16-26-05_771.jpg}
+  \end{center}
+  \mycite{Larry Gonick, The Cartoon Guide to Statistics}
+}\pause
+\only<2>{
+  \begin{center}
+    \includegraphics[width=.8\linewidth]{2012-10-29_16-41-39_523.jpg}
+  \end{center}
+  \mycite{Larry Gonick, The Cartoon Guide to Statistics}
+}\pause
+\only<3>{
+  \begin{center}
+    \includegraphics[width=.8\linewidth]{2012-10-29_16-29-35_312.jpg}
+  \end{center}
+  \mycite{Larry Gonick, The Cartoon Guide to Statistics}
+}
+\end{frame}